The ability to express and recognize emotion via facial expressions is well known to change with age. The present study investigated the differences in the facial recognition and facial expression of the elderly (n = 57) and the young (n = 115) and measure how each group uses different facial muscles for each emotion with Facial Action Coding System (FACS). In facial recognition task, the elderly did not recognize facial expressions better than young people and reported stronger feelings of fear and sad from photographs. In making facial expression task, the elderly rated all their facial expressions as stronger than the younger, but in fact, they expressed strong expressions in fear and anger. Furthermore, the elderly used more muscles in the lower face when making facial expressions than younger people. These results help to understand better how the facial recognition and expression of the elderly change, and show that the elderly do not effectively execute the top-down processing concerning facial expression.

In pattern recognition domain, deep architectures are widely used nowadays and they have achieved fine grades. However, these deep architectures need special demands, especially big datasets and GPU. Aiming to gain better grades without deep networks, we propose a simplified algorithm framework using fusion features extracted from the salient areas of faces. Furthermore, the proposed algorithm has achieved a better result than some deep architectures. For extracting more effective features, this paper firstly defines the salient areas on the faces. This paper normalizes the salient areas of the same location in the faces to the same size, therefore it can gain more similar features from different subjects. LBP and HOG features are extracted from the salient areas, fusion features’ dimensions are reduced by Principal Component Analysis( PCA) and we apply softmax to classify the six basic expressions at once. This paper proposes a salient areas definitude method which uses peak expressions frames to compare with their neutral faces. This paper also proposes and applies the idea of normalizing the salient areas to align the specific areas which express the different expressions. This makes the salient areas found from different subjects have the same size. Besides, gamma correction method is firstly applied on LBP features in our algorithm framework which improves our recognition rates significantly. By applying this algorithm framework, our research has gained state-of-the-art performances on CK+ database and JAFFE database.

In this paper, we study facial expression recognition (FER) using three modalities obtained from the Light Field camera; sub-aperture (SA), depth map, and all-in-focus (AiF) images. Our objective is to construct a more comprehensive and effective FER system by investigating multimodal fusion strategies. For this purpose, we employ EfficientNetV2-S, pre-trained on AffectNet, as our primary convolutional neural network. This model, combined with a BiGRU, is used to process SA images. We evaluate various fusion techniques at both decision and feature levels to assess their effectiveness in enhancing FER accuracy. Our findings show that the model using SA images surpasses state-of-the-art performance, achieving 88.13% ± 7.42% accuracy under the subject-specific evaluation protocol and 91.88% ± 3.25% under the subject-independent evaluation protocol. These results highlight our model's potential in enhancing FER accuracy and robustness, outperforming existing methods. Furthermore, our multimodal fusion approach, integrating SA, AiF and depth images, demonstrates substantial improvements over unimodal models. The decision-level fusion strategy, particularly using average weights, proved most effective, achieving 90.13% ± 4.95% accuracy under the subject-specific evaluation protocol and 93.33% ± 4.92% under the subject-independent evaluation protocol. This approach leverages the complementary strengths of each modality, resulting in a more comprehensive and accurate FER system.

In the present era, there is no longer a necessity to rely on outdated and laborious methods for attendance management in the field of education. Managing attendance for large groups of students in a classroom can be a challenging and time-consuming task when it comes to recording data. Fortunately, modern technologies offer us the opportunity to streamline attendance management and make it more efficient. One effective approach is Real-Time Face Recognition, which simplifies the process of tracking attendance for numerous students daily. We employ the Haar cascade classifier to identify positive and negative facial attributes, along with the LBPH algorithm for accurate face recognition. These techniques are implemented using Python programming language and the OpenCV library while incorporating a user-friendly Tkinter GUI (Graphical User Interface) for seamless usability. By embracing these advancements, we can revolutionize how attendance is managed in educational environments.

This paper proposes a novel facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) module for aggregating global information. The LFN introduces combined channel operations and depthwise convolution techniques, effectively reducing the number of parameters while enhancing feature representation capability. The Spatial Bias module enables the model to focus on local facial features while also capturing the dependencies between different facial regions. Additionally, a novel loss function called Cosine-Harmony Loss is designed. This function optimizes the relative positions of feature vectors in high-dimensional space, resulting in better feature separation and clustering. Experimental results on the AffectNet and RAF-DB datasets show that the proposed LFNSB model performs excellently in facial expression recognition tasks. It achieves high recognition accuracy while significantly reducing the number of parameters, thus substantially lowering model complexity.

Animal welfare is not only an ethically important consideration in good animal husbandry, but can also have a significant effect on an animal’s productivity. The aim of this paper is to show that a reduction in animal welfare, in the form of increased stress, can be identified in pigs from frontal images of the animals. We train a Convolutional Neural Network (CNN) using a leave-one-out design and show that it is able to discriminate between stressed and unstressed pigs with an accuracy of >90% in unseen animals. Grad-CAM is used to identify the animal regions used, and these support those used in manual assessments such as the Pig Grimace Scale. This innovative work paves the way for further work examining both positive and negative welfare states with a view to the development of an automated system that can be used in precision livestock farming to improve animal welfare.

Local binary pattern is one of the visual descriptors and can be used as a powerful feature extractor for texture classification. In this paper, a novel representation for face recognition is proposed, called it Bilateral Line Local Binary Patterns (BL-LBP). This scheme is an extension of Line Local Binary Patterns descriptors in the statistical learning subspace. The present bilateral descriptors are fused with an ensemble learning of calibrated SVM models. The performance of this scheme is evaluated using 5 standard face databases. It is found that it is robust against illumination variation, diverse facial expressions and head pose variations and its recognition accuracy reaches 98 percent, running on a mobile device with a processing speed of 63 ms per face. Results suggest that our proposed method can be very useful for the vision systems that have limited resources where the computational cost is critical.

Emotion recognition defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot communi- cation and many more. The purpose of this study is to propose a new facial emotional recognition model using convolutional neural network. Our proposed model, “ConvNet”, detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. This research focuses on the model’s training accuracy in a short number of epoch which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this work focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases, and we test the system’s success by identifying facial expressions in the real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. ConvNet also achieved validation accuracy of 65% to 70% (considering different datasets used for experiments), resulting in a higher classification accuracy compared to other existing models. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN.

Facial Emotion Recognition (FER) is crucial across psychology, neuroscience, computer vision, and machine learning due to the diversified and subjective nature of emotions, varying considerably across individuals, cultures, and contexts. This paper investigates the impact of histogram equalization, data augmentation and model optimization strategies on three well-known FER datasets using pre-trained VGG models. Additionally, this paper showcases the effectiveness of different regularization techniques, callbacks, and learning schedulers in enhancing model performance by conducting extensive experiments. The model evaluation is discussed in terms of the following metrics: Accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision-Recall Curve (AUC-PRC), and Weighted F1 score. Notably, the fine-tuned VGG models exhibit state-of-the-art performance compared to more complex conventional transfer learning models resulting in accuracies of 100%, 95.92%, and 69.65% on the CK+, KDEF, and FER2013 datasets, respectively.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperpa-rameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperparameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed the advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

Peripheral facial paralysis represents a disabling condition with serious psychological and social impact. Patients with peripheral facial paralysis have a disfigurement of the face with loss of harmony and symmetry, and difficulties in everyday facial functions such as speaking, drinking, laughing, and closing their eyes, with impairment of their quality of life. This paralysis leads to impairment of facial expression that represents one of the first means of communication, an important aspect of human interaction. This review aims to explore the reanimation techniques for managing peripheral facial paralysis. An analysis of static and dynamic techniques for facial reanimation is provided, including muscle flaps, nerve grafting techniques, and bioengineering solutions. Dynamic techniques, when feasible, enable better outcomes in terms of facial motility, although the long-term rehabilitation and the long learning curve. Static techniques, while less invasive, provide limited functional recovery and should be combined with dynamic strategies for good results. Each technique showed its benefits and drawbacks; despite several options for facial reanimation, no technique has been detected as the gold standard. Therefore, each patient must be evaluated on an individual basis, considering his medical history, age, expectations, and treatment goals to find the best and most fitting treatment for him.

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.

This study explores the use of Generative Artificial Intelligence (GAI) in assessing pilot fatigue risk by integrating facial recognition and physiological signals with Inertial Measurement Units (IMUs). By leveraging IMU technology's precise, real-time data on movement and combining it with GAI's advanced data analysis capabilities, the study aims to enhance the accuracy of fatigue prediction models. The analysis reveals that while traditional classifiers like Extreme Random Trees and Random Forests offer modest performance, advanced models such as Support Vector Machines and Naive Bayes demonstrate superior recall rates, highlighting their potential to identify true positives. This integration of AI and IMUs offers a promising approach to developing comprehensive, real-time fatigue monitoring systems, improving safety and efficiency in aviation by providing actionable insights and facilitating more effective fatigue management.

Background/Objectives: Conventional surgery gland using the naked eye remains the standard for submandibular gland resection. Owing to its excellent automatic focus and high magnification, the ORBEYE 3D system introduces precise submandibular resection with less stress. Therefore, we examined the usefulness of the exoscope in submandibular gland resection for the first time. Methods: This retrospective study enrolled 12 patients who underwent submandibular gland resection using an ORBEYE exoscope at the Department of Head and Neck Surgery and Otorhinolaryngology at Kansai Medical University from April 2021 to March 2024. Surgical outcomes were retrospectively reviewed. Results: The mean age of the 12 patients (six females and six males) was 58.5 years (23–83 years). The final histopathology showed pleomorphic adenoma in eight cases, non-sebaceous-type lymphadenoma and carcinoma ex pleomorphic adenoma in one case each, and sialolithiasis with chronic inflammation in two cases. We performed facial artery and vein sparing in all cases except one in which level I neck dissection, including the submandibular gland, was performed. In all cases, we identified the marginal mandibular branches of the facial, hypoglossal, and lingual nerves, as well as the submandibular duct. No postoperative complications occurred in any patient. The mean operative time was 104 min (80–129 min). The mean blood volume lost was 20 mL (5–43 mL). Conclusions: Using ORBEYE in submandibular gland resection will contribute to preserving the mandibular marginal branch of the facial and lingual nerves and the facial artery and vein, and visualizing the submandibular duct, which are important structures surrounding the submandibular gland.

Manufacturing of facial epithesis is a complex technological process including several traditional stages of production, accompanied by aggravated psycho-emotional status of the patient. One of the main factors causing discomfort in patients with facial defects is the process of obtaining silicone impressions from the wound surface and the long waiting period for the final epithesis. Active development of digital technologies in medicine and dentistry helps specialists not only to automate most of the traditional stages of manufacturing of dento-mandibular and facial prostheses, but also improves the quality of life of patients by increasing the efficiency of services and reducing the production time of prosthetic structures. The application of digital technologies in the production of facial epithesis was difficult due to the lack of specialized 3D-software for modeling and material for further additive manufacturing of structures. The authors developed a unique technology for 3D modeling and additive manufacturing of facial epithesis, including a 3D CAD module and material for 3D printing. The primary objective was to fabricate immediate facial prostheses in the postoperative period in the shortest possible time until the final epithesis was fabricated.

Facial expressions play a key role in interpersonal communication when it comes to negotiating our emotions and intentions, as well as interpreting those of others. Research has shown that we can connect to other people better when we exhibit signs of empathy and facial mimicry. However, the relationship between empathy and facial mimicry is still debated. Among the factors contributing to the difference in results across existing studies is the use of different instruments for measuring both empathy and facial mimicry, as well as often ignoring the differences across various demographic groups. This study first looks at the differences in empathetic abilities of people across different demographic groups based on gender, ethnicity and age. The empathetic ability is measured based on the Empathy Quotient capturing a balanced representation of both emotional and cognitive empathy. Using statistical and machine learning methods, the study then investigates the correlation between the empathetic ability and facial mimicry of subjects in response to images portraying different emotions displayed on a computer screen. Unlike the existing studies measuring facial mimicry using electromyography, this study employs a technology detecting facial expressions based on video capture and deep learning. This choice was made in the context of increased online communication during and post the COVID-19 pandemic. The results of this study confirm the previously reported difference in the empathetic ability between females and males. However, no significant difference in the empathetic ability was found across different age and ethnic groups. Furthermore, no strong correlation was found between empathy and facial reactions to faces portraying different emotions shown on a computer screen. Overall, the results of this study can be used to inform the design of online communication technologies and tools for training empathy team leaders, educators, social, and health care providers.

Polyetheretherketone (PEEK) in the last few years emerged as an exceedingly promising material for craniofacial defect, due to its biocompatibility and mechanical properties. However, its utilization remains controversial due to its inertness and low osteoinductivity. This study aimed to investigate the postoperative outcomes of patients undergoing maxillo-facial and neurosurgical procedures with PEEK implants. The focus is on evaluating bone regrowth on the surface and edges of the implant, periosteal reactions, and implant positioning. A retrospective analysis of 12 maxillo-facial surgery patients and 10 neurosurgery patients who received PEEK implants was conducted. CT scans performed at least one-year post-operation, were examined for bone regrowth, periosteal reactions, and implant positioning. In maxillo-facial cases, the analysis included mandibular angle and fronto-orbital reconstruction, while neurosurgical cases involved cranioplasty. In maxillofacial surgery, 11 out of 12 patients showed radiological evidence of bone regrowth around PEEK implants, with favorable outcomes observed in craniofacial reconstruction. In neurosurgery, 9 out of 10 patients exhibited minimal or none bone regrowth, while one case demonstrated notable bone regeneration beneath the PEEK implant interface. The study highlights the importance of implant design and patient-specific factors in achieving successful outcomes, providing valuable insights for future implant-based procedures.

The integration of Artificial Intelligence (AI) in dairy farm management through biometric facial recognition for cows is a significant stride in livestock management. This review critically evaluates the evolution, applications, and challenges of AI-driven biometric facial recognition in dairy farming. It emphasizes the role of this technology in enhancing individual monitoring of dairy cows, providing accurate health, behavior, and productivity tracking. Originally derived from human facial recognition systems, this approach utilizes distinctive bovine facial features for essential, non-invasive, real-time monitoring in large-scale operations. The progression of AI from elementary pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning models marks a shift toward data-driven farming. This study addresses key challenges such as environmental variability, data collection hurdles, ethical concerns, and technological limitations. It also contrasts various AI models, spotlighting their unique strengths and practical utility in dairy farming scenarios. Despite these challenges, facial recognition technology holds promise for improving farm efficiency, animal welfare, and sustainable practices, highlighting the need for continuous research and development. The review concludes by advocating for future research focused on environmental adaptability, ethical AI application, cross-breed compatibility, and integration with other farming technologies. Ultimately, it underscores AI's transformative potential in modernizing dairy farming towards a more data-oriented, responsible agricultural future.

With the rapid growth of the global mask market, consumers have put forward higher requirements for the functionality and ingredients of mask products. Conventional facial masks generally use ordinary non-woven fabrics as the mask base fabric, are frequently used with essence and contain preservatives. At the same time, they are susceptible to the influence of the external environment and are easily oxidized, which will cause the mask to deteriorate and skin allergic reactions. In addition, conventional facial masks exhibit poor skin fit, limited breathability, inadequate absorption of nutrient solutions, and easy to drip. The nanofiber mask prepared by electrospinning technology has a high specific surface area and high porosity, which enabling improved nutrient absorption by the skin, superior skin fit, and exceptional breathability. Nanofiber facial mask products can effectively solve consumer pain points and promote the high-end development of facial mask products. Therefore, this research combines needleless electrospinning technology to develop a novel solid-state, preservative-free, and quick-dissolving nanofiber facial mask that can be prepared on a large scale. Based on needleless electrospinning technology, this research deeply explores the process parameters and influencing mechanisms of quick-dissolving nanofiber facial masks, ultimately achieving the consistent production of nanofiber facial masks with optimal morphology. Comprehensive and evaluation of the structure and performance of nanofiber facial masks from micro and macro perspectives. The experimental results show that the mask morphology is optimal under the process conditions of using a spinning liquid of 20% collagen peptide solution, a spinning voltage of 30kV, a collection distance of 19cm, and a liquid supply speed of 130ml/h.

Objective: There are currently no known treatment methods for ensuring complete recovery from facial nerve damage. Thus, various basic and clinical studies continue to be conducted to achieve this end. As part of this research, autophagy is being investigated for its role in nerve regeneration. Here, we review the existing literature regarding the involvement and signifi-cance of autophagy in facial nerve injury and regeneration. Methods: A comprehensive litera-ture review was conducted to assess the induction and role of autophagy in facial nerve injury and subsequent regeneration. Results: Studies conducted on rats and mice have demonstrated activation of autophagy and expression of related factors in facial and sciatic nerves. Among the most studied of these factors in relation to degeneration and regeneration of facial and sciatic nerves are LC3II/I, PI3K, mTOR, Beclin-1, ATG3, ATG5, ATG7, ATG9, and ATG12. Conclusion: This analysis indicates that autophagy is involved in the process of nerve regeneration follow-ing facial and sciatic nerve damage. Inadequate autophagy induction or failure of autophagy re-sponses can result in regeneration issues after facial or sciatic nerve damage. Animal studies suggest that autophagy plays an important role in facial and sciatic nerve degeneration and re-generation.

There is a growing consensus that culture influences the perception of facial expressions of emotion. However, little is known about whether and how culture shapes the production of emotional facial expressions, and even less so about whether culture differentially shapes the production of posed versus spontaneous expressions. Drawing on prior work on cultural differences in emotional communication, we tested the prediction that people from the Netherlands (a historically heterogeneous culture where people are prone to low-context communication) produce facial expressions that are more distinct across emotions compared to people from China (a historically homogeneous culture where people are prone to high-context communication). Furthermore, we examined whether the degree of distinctiveness varies across posed and spontaneous expressions. Dutch and Chinese participants were instructed to either pose facial expressions of anger and disgust, or to share autobiographical events that elicited spontaneous expressions of anger or disgust. Using the complementary approaches of supervised machine learning and information-theoretic analysis of facial muscle movements, we show that posed and spontaneous facial expressions of anger and disgust were more distinct when produced by Dutch compared to Chinese participants. These findings shed new light on the role of culture in emotional communication by demonstrating, for the first time, effects on the distinctiveness of production of facial expressions.

The purpose of this study is to develop a cranial traction therapy program to help correct facial asymmetry of the hard tissues through the means of the treatment of soft tissues—a non-surgical therapeutic method for the correcting of facial asymmetry. We have formed a group of experts who have agreed to the study. In the primary survey, open questions were used. In the second survey, the results of the first survey were summarized and the degree of agreement was presented to the questions in each category. In the third survey, we conducted a statistical analysis of the degree of agreement on each item of question. All surveys also performed email. The distribution was calculated using the SPSS (ver.23.0) program, and the mean difference between the result and X² was calculated. The significance level was set to p<.05. Most of the questions attained a certain level of consensus by the experts (average of 4.0 or higher), it can be said that most are important and suitable questions. the results regarding the degree of importance for each of the points of evaluation made by the groups of experts in both the second and third stage of the cranial traction therapy program were verified using content validity ratio (CVR 77). The ratio for the cranial traction 13 points of evaluation was within the range of 0.40∼1.00, so the Delphi program for the cranial traction therapy verified that the content was valid.

Previous studies have indicated that facial expressions can serve as an objective evaluation method for hedonics (overall pleasure) of food and beverages. In this study, we aimed to validate the findings of our previous research, which demonstrated that facial expressions induced by tastants can predict the perceived hedonic ratings of these tastants. Facial expressions of 29 female participants (aged 18-55 years) were recorded using a digital camera while they consumed 12 different concentrations of solutions representing five basic tastes. The facial expressions were then analyzed using the widely-used facial expression analysis application, FaceReader, to identify seven emotions (surprise, happiness, scare, neutral, disgust, sadness, and anger) with scores ranging from 0 to 1. Participants also rated the hedonics of each solution on a scale from -5 (extremely unpleasant) to +5 (extremely pleasant). A multiple linear regression analysis was conducted to develop a formula to predict perceived hedonic ratings. The formula's applicability was tested by analyzing emotion scores for 11 additional taste solutions consumed by 20 other participants. The predicted hedonic ratings demonstrated good correlation and concordance with the perceived ratings, supporting the validity of our previous findings using different software and taste stimuli among diverse participants.

We live in extraordinary times, where COVID-19 pandemic has brought the whole world to a screeching halt. Tensions and contradictions that surround the pandemic ridden world include the availability, and the lack thereof, various facial protection measures to mitigate the viral spread. Here, we comprehensively explore the different type of facial protection measures, including masks, needed both for the pubic and the health care workers (HCW). We discuss the anatomy, the critical issues of disinfection and reusability of masks, the alternative equipment available for the protection of the facial region from airborne diseases, such as face shields and powered air purifying respirators (PAPR), and the skin-health impact of prolonged wearing of facial protection by HCW. Clearly, facial protection, either in the form of masks or alternates, appears to have mitigated the pandemic as seen from the minimal COVID-19 spread in countries where public mask wearing is strictly enforced. On the contrary, the healthcare systems, that appear to have been unprepared for emergencies of this nature, should be appropriately geared to handle the imbalance of supply and demand of personal protective equipment including face masks. These are two crucial lessons we can learn from this tragic experience.

Facial artificial intelligence (AI) skincare applications offer patients simple and effective means of obtaining quantitative information regarding their skin characteristics. Advances in AI may facilitate dermatologic assessments by providing accurate and reliable scores for common skin concerns. The objective of our study was to assess the effect of different lighting on facial characteristic scores produced by an iPad-based AI skin analysis system. Participants were recruited from dermatology clinics. Images were captured using an AI skincare app in different lighting settings: below average lighting (L1), standard room lighting (L2), and enhanced studio lighting (L3). Raw scores produced for twenty facial characteristics were recorded. Higher raw scores indicated better skin health. Scores were compared between the three lighting conditions. Below average lighting had the highest mean raw scores for facial characteristics (79.67 ±10.56). Mean raw scores were lowest for enhanced studio lighting conditions (74.43 ±10.24). The difference in scores produced by the different lighting conditions was not statistically significant (p=0.79). Enhanced studio lighting did not appear necessary as scores were similar without the additional lighting. The application used in our study may serve as a cost-effective and convenient tool both within and outside clinical settings allowing enhanced quantitative analysis of facial characteristics.

There is a considerable increase in the use of substances and medical procedures aimed at changing the esthetics of the face, particularly the appearance of the lips. Permanent fillers such as polydimethylsiloxane, also called liquid silicone, are widely used, but their application for facial esthetics is currently obsolete. However, the long-term complications remain a reality, as they can occur decades after the application. Thus, the goal of this case report was to present a complication after 16 years of treatment using liquid silicone. This case report involved a 52-year-old male with a complication of bilateral permanent filler in the upper lip performed 16 years ago, its surgical removal, and histological analysis. Histologically, it was possible to identify a chronic inflammatory, lymphoplasmacytic, and granulomatous reaction, with foreign body giant cells’ reaction, in relation to non-polarizable exogenous material due to the reaction to silicone. The most common complications are granulomas' appearance and material displacement. The case report shows these granulomas are characterized as chronic low-caliber inflammation around the silicone. They have an unknown etiology but are probably multifactorial, from continuous trauma, friction or irritation, iatrogenic factors, infection, immunological mechanisms, and genetic and molecular variations, and can be highly related to the impurity of the injected material.

This paper investigates nonverbal communication in human interactions, with a specific focus on facial expressions. Employing a Long Short-Term Memory (LSTM) architecture and a custom-ized facial expression framework, our approach aims to improve virtual agent interactions by incorporating subtle nonverbal cues. The paper contributes to the emerging field of facial expres-sion generation, addressing gaps in current research and presenting a novel framework within Unreal Engine 5. The model's architecture, trained on the CANDOR corpus, captures temporal dynamics, and refines hyperparameters for optimal performance. During testing, the trained model showed a cosine similarity of -0.95. This enables the algorithm to accurately respond to non-verbal cues and interact with humans in a way that is comparable to human-human interac-tion. Unlike other approaches in the field of facial expression generation, the presented method is more comprehensive and enables the integration of a multi-modal approach for generating facial expressions. Future work involves integrating blendshape generation, real-world testing, and the inclusion of additional modalities to create a comprehensive framework for seamless hu-man-agent interactions beyond facial expressions.

Animals’ facial expressions have been widely used as a readout for emotion. Scientific interest in the facial expressions of laboratory animals has centered primarily on negative experiences, such as pain, experienced as a result of scientific research procedures. Recent attempts to standardize evaluation of facial expressions associated with pain in laboratory animals has culminated in the development of “grimace scales”. In the context of laboratory animals, these have been developed and evaluated for mice, rats, rabbits, sheep, and ferrets. The prevention or relief of pain in laboratory animals is a fundamental requirement for in vivo research to satisfy community expectations. However, to date it appears that the grimace scales have not seen widespread implementation as clinical pain assessment techniques in biomedical research. In this review, we discuss some of the barriers to implementation of the scales in clinical laboratory animal medicine, progress made in automation of collection, and suggest avenues for future research.

One in 12 babies is born with a rare genetic disease. Sadly, most cases are undetected until later age, missing time for early treatment and opportunity to prevent complications. Humanity has entered a new era where Big Data collected by governments, including 2D and 3D facial scans, are available. Many rare genetic diseases can be identified by artificial intelligence (AI) analysis of the facial photo. Phenotyping AI utilizations facilitate comprehensive and accurate genetic evaluations. AI processing of this Big Data to identify rare genetic diseases could bring unimaginable benefits to healthcare, although this would be a questionable step in terms of citizen privacy and could lead to future "Orwellian" ramifications with government abuse. Going forward, a balance must be found between protecting the privacy of citizens and the enticing use of AI for their health risks and cost savings through prevention. The unimaginable potential of AI early diagnostics from facial photos also raises various ethical and legal concerns. This paper presents concept, protentional methods and legal and other limitations within EU legal framework in contrast with potential benefits. This paper is focused on AI utilization to early diagnostic of rare genetic diseases. Shift of paradigm in the screening for rare genetic diseases in population with AI face analysis is expected to have a significant impact. The potential of AI algorithms utilizations similar to face2gene app in general population or systematically on Big governmental datasets recording facial traits changes in time can have significant impact on public health but at the same time give raise to profound concern as violation of one’s privacy.

The primary objective of this systematic review study was to investigate the effectiveness, durability, and adverse events of PLLA treatment for aesthetic indications. The search strategy was performed in MEDLINE (Ovid). The electronic literature search of five databases was performed, from the inception of the databases until 12th of February 2024. This was to identify randomized clinical trials that assessed PLLA treatment in adult individuals exhibiting facial aging and/or facial lipoatrophy. Risk of bias was assessed using the Cochrane Risk-of-Bias Tool for Randomized Trials (RoB 2). Eleven RCTs out of 1467 identified citations were included. Four studies showed increased dermal thickness, significant improvement in facial lipoatrophy severity and aesthetic clinical scores, after PLLA treatment with its effects sustained for at least 25 months. Two studies demonstrated the superiority of PLLA over injectable human collagen. Also, three studies showed positive results favoring PLLA when compared with PH gel in lipoatrophy severity, trans epidermal water loss, skin quality, elasticity, and patient satisfaction. All adverse events were mild-to-moderate in intensity, and the main ones worth noting were bruising, hematoma, tenderness, nodules, and edema. Five out of 11 studies were considered as high risk of bias. The evidence on the effectiveness and safety of PLLA for facial rejuvenation is of low quality; thus, the reported high effectiveness, safety, and long-lasting effects for this purpose, should be further investigated.

According to published descriptions, one of the most incapacitating forms of orofacial pain is trigeminal neuralgia (TN). At its core, trigeminal neuropathic pain (TN) is still a clinical diagnostic that has to be differentiated from other forms of TN and face pain linked to other neuralgias or headache disorders. Imaging can only help with the cause of the pain, whether it’s a vascular origin or not. It is becoming more and more evident that there is no one-size-fits-all medication or surgical procedure that can effectively treat every patient with trigeminal neuralgia (TN). This is probably because TN is a diverse collection of conditions that all present with face discomfort. Medical therapy using anticonvulsants like carbamazepine is still the first-line treatment for TN. If this doesn't work for the patient, surgical treatments are available. Microvascular decompression is often a safe, efficient operation with results that are both rapid and long-lasting. Patients who may benefit from percutaneous techniques such as radiofrequency ablation, glycerol ablation, balloon compression, or a combination, include those who cannot undergo general anesthesia or whose medical conditions prohibit a suboccipital craniectomy. Radiosurgery may be a great option for patients with bleeding diathesis brought on by blood-thinning medications who are not eligible for invasive procedures or who do not want to undergo open surgical procedures, as long as the patient is aware that achieving maximum pain relief may take several months. In conclusion, peripheral neurectomies persist in offering a low-cost and resource-saving substitute for pain management to those residing in areas with restricted financial and healthcare resources. In the end, developing a better understanding of the molecular processes behind trigeminal neuralgia will lead to new, less intrusive, and more effective treatments.

Keywords: Rhinoplasty - Augmentation rhinoplasty - Open rhinoplasty - Facial rejuvenation – Centrofacial lipofilling.

Abstract: The pursuit of youth and attractiveness is a fundamental desire in contemporary society. For many individuals, the appearance of visible signs of aging, such as skin laxity, gravitational changes in facial and neck soft tissues, and loss of harmonious proportions, can be psychologically distressing. The choice of effective methods for addressing these changes depends on their specific characteristics, the patient's anatomical features, and the type of facial aging. The introduction of 3D imaging to determine the hyoid bone level, the volume of soft tissue structures in the cervico-mental area, ptosis of subplatysmal structures, and the volume of submandibular glands, in addition to ultrasound study of the submental projection at the preoperative stage, are key for taking the best decision regarding preoperative planning. This case report aims to illustrate how modern imaging techniques are a fundamental part of the preoperative assessment of the mid and lower third of the face to ensure the most favorable aesthetic outcomes for each patient. Key message: To attain harmonious results in surgical correction of age-related facial and neck changes, it is crucial to consider the patient's anatomical features and the extent of age-related alterations. A comprehensive facial analysis, taking into account the type of facial aging and employing a CT scan and ultrasound examination of the soft tissues in the chin region, facilitates the development of an individualized plan tailored to achieve the patient's desired outcome.

Temporomandibular disorders (TMD) is a group of orofacial pain conditions which is the most common non-dental pain complaint in the maxillofacial region. Due to the complexity of the etiology and often cyclical nature of the disease, the diagnosis and management of TMD remain a challenge where consensus is still lacking in many aspects. While clinical examination is considered the most important process in the diagnosis of TMD, imaging may serve as a valuable adjunct in selected cases. Depending on the type of TMD, many treatment modalities have been proposed, ranging from conservative options to open surgical procedures. In this review, the authors discuss the present thinking in the etiology and classification of TMD, followed by the diagnostic approach and the current trend and controversies in management.

Parkinson’s disease (PD) is typically well-recognized by its characteristic motor symptoms (e.g., bradykinesia, rigidity, and tremor). The cognitive symptoms of PD are increasingly being acknowledged by clinicians and researchers alike. However, PD also involves a host of emotional and communicative changes which can cause major disruptions to social functioning. These include problems producing emotional facial expressions (i.e., facial masking) and emotional speech (i.e., dysarthria), as well as difficulties recognizing the verbal and non-verbal emotional cues of others. These social symptoms of PD can result in severe negative social consequences, including stigma, dehumanization, and loneliness, which might affect quality of life to an even greater extent than more well-recognized motor or cognitive symptoms. It is therefore imperative that researchers and clinicians become aware of these potential social symptoms and their negative effects, in order to properly investigate and manage the socioemotional aspects of PD. The present review provides an examination of the current research surrounding some of the most common social symptoms of PD and their related social consequences, and argues that proactively and adequately addressing these issues might improve disease outcomes.

Introduction: Obstructive sleep apnea syndrome (OSAS) is characterized by recurrent episodes of upper airway obstruction and involves intermittent hypoxia and sleep fragmentation, with potentially serious consequences for affected patients. (1–3). Although nocturnal polysomnography is the gold standard, it is an exam that is not readily available and is very expensive. (4). Hence, the need to find new diagnostic methods. Considering that the causes of OSAS can be divided into anatomical-structural ones, related to craniofacial anomalies, it has been hypothesized that anthropometric measurements taken by new 3D stereophotogrammetric systems could be useful as a diagnostic tool in pediatric patients with OSAS (5) (6). The primary objective of the study is, therefore, to evaluate the correlation between mandibular length measured between menton and gonion, obtained through 3D stereophotogrammetry using the "Face Scanner maxi 6", and the severity of OSAS (AHI classification), defined by the score of the Sleep Clinical Record (SCR) questionnaire in a group of pediatric patients with suspected obstructive sleep disorders. The secondary objective concerns the identification of possible correlations between other mandibular parameters estimated by 3D stereophotogrammetry and the severity of OSAS, defined by the McGill Oximetry Score (MOS), and the association between MOS and SCR to determine the AHI level. Materials and methods: The study considered patients who were prescribed a pulseoximetry exam due to suspected obstructive sleep disorders. All participating patients underwent a specialized orthodontic examination, which included completing the SCR questionnaire and taking facial photographs using a 3D stereophotogrammetric system. The SCR questionnaire considers 11 dental, nasal, oropharyngeal, and facial phenotype parameters to determine a final score from 0 to 18, indicating the risk of obstructive sleep apnea syndrome. The acquired stereophotogrammetric images were processed using dedicated software to obtain a three-dimensional model representing the patient's facial phenotype. Subsequently, anatomical landmarks and relevant anthropometric measurements were identified on the model, and it was assessed whether one or more of these showed concordance criteria with the parameters derived from SCR and MOS. The anthropometric evaluation was performed by expert operators, taking into account mandibular variables. Results: Fifteen patients were analyzed, with 2 excluded due to alterations in the stereophotogrammetric images caused by poor patient cooperation during the acquisition of the photographs. Spearman's correlation analysis was performed to investigate the relationship between anthropometric measurements, obtained by 3D stereophotogrammetry, and OSAS severity, by means of SCR, MOS, and SCR+MOS. A significant and negative correlation was observed between right mandibular length (gonion-menton) and MOS (ρ=−0.586, p=0.036). Conclusions: The result obtained has important implications for the field of study and suggests that future attention should focus more on the mandibular length variable as a determining factor for OSAS. However, the sample examined was relatively small, which might obscure the correlation with contralateral mandibular length and potential correlations with other mandibular/facial parameters. Additionally, the small sample size prevented stratification by sex and age. The study thus provides an important contribution to understanding the relationships between 3D stereophotogrammetry and pediatric OSAS but also highlights the need for further research that could potentially influence future developments in diagnosing OSAS in children.

The diagnosis of migraine is based on clear criteria outlined in the International Classification of Headache Disorders version 3 (ICHD-3). Notably, the criteria in ICHD-3 omit the location of migraine. There are increasing reports of migraine in the facial region.(1-3) Facial presentations of migraine are not easy to diagnose as they appear in the lower 2/3rd of the face, often in the maxillary sinus region, around the ear, the upper/lower jaws and teeth.(4) Moreover, the symptomatology of these facial representations of these headaches often resembles sinusitis and dental pathology. We will review these presentations, their diagnosis and possible pathophysiology.

In this study, we investigate how dataset composition influences the performance and bias of age estimation models across different ethnic groups. We utilized pre-trained Convolutional Neural Networks (CNNs) such as VGG19, fine-tuning them on two datasets: UTKFace and APPA-REAL. UTKFace comprises 23,705 samples (12,391 males, 11,314 females) including 10,078 White, 4,526 Black, and 3,434 Asian individuals. APPA-REAL consists of 7,591 samples (3,818 males, 3,773 females) with 6,686 White, 231 Black, and 674 Asian individuals. We adjusted dataset composition by oversampling minority groups and reducing samples from the majority group to evaluate effects on model performance, measured using Mean Absolute Error (MAE) and standard deviation. Our findings demonstrate that oversampling alone does not guarantee equitable performance across ethnicities; reducing samples from the majority group often resulted in more balanced performance, evidenced by lower MAE standard deviations. These results underscore the importance of a nuanced approach to achieve fairness, including combining various sampling techniques. This study emphasizes the critical need for tailored dataset compositions to mitigate bias and suggests exploring more refined data processing methods and algorithmic adjustments for enhanced model fairness and accuracy in facial recognition technologies.

In recent years, there has been a growing interest in the prediction of facial age due to its diverse applications in fields such as security, entertainment, and healthcare. The current scourge of underaged voting in Nigeria is a problem and this research delves into the realm of facial age prediction by employing four well-known convolutional neural network (CNN) architectures, namely VGG16, ResNet50, Mobile Net, and VGG19 to predict chronological ages from facial pictures of person. The objective is to achieve precise age estimation from facial images, utilizing two datasets: UTKFace and CASIA African Facial Datasets. The results of this investigation are noteworthy. Specifically, the VGG16 model which demonstrated remarkable performance, yielding a Mean Absolute Error (MAE) of 1.76 when applied to the UTK-Face dataset. Additionally, when utilizing Mobile-Net, an unprecedented MAE of 1.10 was achieved for the Casia Africa Face Dataset. Notably, this marks the first instance of employing the dataset for facial age detection with CNNs, and this approach outperformed previous works, yielding the lowest MAE among all the studies reviewed.

Facial emotion detection is a challenging task that deals with emotion recognition. It has applications in various domains, such as behavior analysis, surveillance systems and human-computer interaction (HCI). Numerous studies have been implemented to detect emotions, including classical machine learning algorithms and advanced deep learning algorithms. For the machine learning algorithm, the hand-crafted feature needs to be extracted, which is a tiring task and requires human effort. Whereas in deep learning models, automated feature extraction is employed from samples. Therefore, in this study, we have proposed a novel and efficient deep learning model based on Neural Architecture Search Network utilizing superior artificial networks such as RNN and child networks. We performed the training utilizing the FER 2013 dataset comprising seven classes: happy, angry, neutral, sad, surprise, fear, and disgust. Furthermore, we analyzed the robustness of the proposed model on CK+ datasets and comparing with existing techniques. Due to the implication of reinforcement learning in the network, most representative features are extracted from the sample network. It extracts all key features without losing the key information. Our proposed model is based on one stage classifier and performs efficient classification. Our technique outperformed the existing models attaining an accuracy of 98.14%, recall of 97.57%, and precision of 97.84%.

(1) Background: The COVID-19 pandemic has affected healthcare utilization rates on a global scale. This study aimed to analyze whether the COVID-19 pandemic affected the characteristics of first-visit patients with peripheral facial palsy visiting a Korean Medicine hospital and to observe characteristics changes in them. (2) Methods: This study analyzed the electronic medical records of 2,310 first-visit patients with peripheral facial palsy who visited the Facial Palsy Center, Korean Medicine Hospital, Kyung Hee University Medical Center from January 1, 2019 to December 31, 2021 in terms of demographic characteristics, disease phase, residence locations, hospital visit route, and patient care type. (3) Results: There were significant relationships between the presence of the COVID-19 pandemic (i.e., before and during) and the number of patients by disease phase (p=0.043), residence locations (p=0.003), residence locations in Seoul (p=0.003), and patient care types (p=0.003). (4) Conclusions: There were several differences in characteristics of first-visit patients with peripheral facial palsy visiting a Korean Medicine hospital during COVID-19 pandemic in terms of demographic characteristics, disease phase, residence locations, residence locations in Seoul, and patient care types.

Single-level Principal Components Analysis (PCA) and multi-level PCA (mPCA) methods are applied here to a set of (2D frontal) facial images from a group of 80 Finnish subjects (34 male; 46 female) with two different facial expressions (smiling and neutral) per subject. Inspection of eigenvalues gives insight into the importance of different factors affecting shapes, including: biological sex, facial expression (neutral versus smiling), and all other variations. Biological sex and facial expression are shown to be reflected in those components at appropriate levels of the mPCA model. Dynamic 3D shape data for all phases of a smile made up a second dataset sampled from 60 adult British subjects (31 male; 29 female). Modes of variation reflected the act of smiling at the correct level of the mPCA model. Seven phases of the dynamic smiles are identified: rest pre-smile, onset 1 (acceleration), onset 2 (deceleration), apex, offset 1 (acceleration), offset 2 (deceleration), and rest post-smile. A clear cycle is observed in standardized scores at an appropriate level for mPCA and in single-level PCA. mPCA can be used to study static shapes and images, as well as dynamic changes in shape. It gave us much insight into the question “what’s in a smile?”

The correlation between pain and emotional status in subjects with temporomandibular disorders (TMD) is known. Regardless of that up to date these findings were seldomly confronted with the objective assessment of pain using a digital algometer on the mastication muscles. The aim of the study was to investigate the relationship between the emotional status, chronic pain pain pressure threshold (PPT). 38 subjects were screened for the existence of temporomandibular disorders with the 3Q/TMD questionnaire, for chronic pain (GCPS) and emotional status (PHQ-9 and GAD-7). The results were confronted with the PPT measured with the digital algometer on the masseter muscles. Each subject was examined three times (after 1 month and after 7 months from the initial exami-nation session. Chronic pain scores (GCPS) were found to be higher in the TMD group in all three examination sessions (p<0,05). The pain, depression and anxiety levels were higher in females de-spite the existence of TMD, but only some were significant.A correlation between chronic orofacial pain scores and PPT was found in 2 out of 3 examination sessions. The PPT scores on the masseter muscles did not correlate with the emotional state in the study population.

Primary lymphoma of the middle ear is rare in cats, and little information has been accumulated on the treatment and course. An 11-year-old spayed female mixed-breed cat was brought to our hospital with facial nerve paralysis, which had been stable using prednisolone for 3 months. She was diagnosed with B-cell lymphoma of the right middle ear after otoscopic sampling, which showed evidence of filling of bilateral tympanic bullae on computed tomography. Hepatic metastasis was suspected and she was treated with multidrug chemotherapy, resulting in clinical remission. On day 176, magnetic resonance imaging and computed tomography was performed as a checkup, revealing tumor invasion into the nasopharyngeal region and recurrence of hepatic lesions without any brain abnormality. Nasal congestion then worsened and the patient died on day 228 after presenting with generalized seizures. Clinicians should be aware of the possibility of invasion to surrounding tissues and hepatic metastasis in cases of feline primary middle ear lymphoma.

Ciliopathies are genetic syndromes that link skeletal dysplasias to dysfunction of primary cilia. Primary cilia are sensory organelles synthesized by intraflagellar transport (IFT) - A and B complexes, which traffic protein cargo along a microtubular core. We have reported that deletion of IFT-A gene, Thm2, together with a null allele of its paralog, Thm1, causes a small skeleton with small mandible or micrognathia in juvenile mice. Using micro-computed tomography, here we quantify the craniofacial defects of Thm2^-/-;Thm1^aln/- triple allele mutant mice. At postnatal day 14, triple allele mutant mice exhibit micrognathia, maxillary hypoplasia, and a decreased facial angle due to shortened maxilla, premaxilla, and nasal bones, reflecting altered development of facial anterior-posterior elements. In contrast, other ciliopathy-related craniofacial defects, such as cleft lip and/or palate, hypo-/hypertelorism, broad nasal bridge, craniosynostosis, and facial asymmetry, were not observed, suggesting development of the facial transverse dimension is intact. Calvarial-derived osteoblasts of triple allele mutant mice showed reduced bone formation in vitro that was ameliorated by Hedgehog agonist, SAG. Together, these data indicate that Thm2 and Thm1 genetically interact to regulate bone formation and sculpting of the postnatal face. The triple allele mutant mice present as a novel model to study craniofacial bone development.

Safety and the apprehension of crime persist as significant concerns in both urban and rural locales. Crime Prevention Through Environmental Design (CPTED) guidelines, an architectural approach to crime deterrence, mitigate the likelihood of criminal activity by implementing strategic defensive design plans, particularly through effective lighting schemes within urban settings, thereby alleviating fear of crime. This comprehensive set of principles serves to enhance overall quality of life. Additionally, within the realm of environmental crime prevention, enhanced street lighting has been identified as a crucial factor in diminishing both actual crime rates and the perceived risk of criminal activity in built environments. While current recommendations advocate for the installation of lighting in poorly lit areas for safety reasons, evaluating visibility solely based on the presence of existing streetlights proves insufficient for analyzing road surface luminance comprehensively. Thus, this study proposes establishing standardized illumination levels specifically tailored for facial recognition of potential criminal offenders. Furthermore, we advocate for defining specific thresholds for color temperature and colorimetric criteria to aid in distinguishing characteristics of individuals' attire. To analyze photometric safety and crime frequency in Seoul, Korea, new primary evaluation indicators were developed, including the Brightness index, Informatization index, and Safety index. By incorporating quantitative data into lighting design strategies, particularly within the framework of CPTED, there exists the potential to mitigate fear of crime during nighttime hours. These strategies facilitate the creation of well-lit environments conducive to the swift recognition and identification of potential perpetrators, thereby presenting a proactive approach to crime prevention. Consequently, this study introduces quantitative lighting standards to augment the efficacy of CPTED guidelines, ultimately contributing to the reduction of crime incidence.

Detection and measurement of pain are crucial in designing effective pain management interventions to alleviate suffering and prevent functional decline in the medical domain. Traditional methods of pain identification rely on verbal patient reports or observable signs, which can be imprecise or subjective due to variability in patient self-assessments. Hence, there is a pressing need for automated methods to standardize pain measurement procedures. This paper proposes a novel facial expression-based automated pain assessment system as a behavioral indicator for pain evaluation. Our system integrates a fusion structure comprising Convolutional Neural Networks (CNNs) and Tensor-based extended Quantitative Expression Descriptors Analysis (TXQEDA). This innovative approach allows for joint learning of robust pain-related facial expression features from raw facial images, combining RGB appearance with shape-based latent representation. We extensively evaluated our proposed model using the UNBC-McMaster dataset for pain classification and intensity estimation. Our results demonstrate the efficacy of the proposed technique, achieving an impressive accuracy of 99.10% for pain level classification. Furthermore, our system excels in pain intensity estimation, showcasing its potential to provide precise and reliable assessments of pain severity. Overall, our research underscores the importance of automated pain assessment systems in improving patient care and clinical outcomes. By leveraging advanced deep learning techniques and fusion strategies, our proposed model offers a promising approach for standardizing pain measurement procedures and enhancing the quality of pain management interventions.

Treatment techniques for facial paralysis have evolved significantly over the past few decades, offering a variety of options to improve facial symmetry and muscle function. The purpose of this paper is to introduce a novel static treatment technique for the surface of the nasolabial fold (NLF) in patients with chronic facial nerve paralysis, aged between 40 and 80 years, who are not candidates for dynamic rehabilitation and present with NLF erasure. The surgical marking process was initiated by determining the projection of the nasolabial fold on the paralyzed side, through measurement of the NLF on the non-paralyzed side. Subsequently, a line was drawn around the nasal ala, and a second line was demarcated in the medial region of the sulcus. In the region close to the nasal ala, along this second line, a notch was planned. The procedure was conducted under local anesthesia. In this study, four procedures were performed without any complications. Comparatively, the outcomes demonstrated an improvement in facial symmetry, the creation of a new NLF, elevation of the ipsilateral lip commissure, and correction of nasal deviation. The proposed technique is straightforward, reproducible, and can be performed in an outpatient setting.

(1) Background: Painful nervus intermedius neuropathy (e.g., geniculate neuralgia) involves continuous or near-continuous pain affecting the distribution of the intermedius nerve(s). The diagnosis of this entity is challenging, particularly when the clinical and demographic features do not resemble the typical presentation of this condition. To the best of our knowledge, only three case reports have described the occurrence of nervus intermedius neuropathy in young patients. (2) Case Description: A 13-year-old female referred to the Orofacial Pain clinic with a complaint of pain located deep in the right ear and mastoid area. The pain was described as a constant throbbing and dull, with an intensity of 7/10 on numerical rating scale, characterized by superimposed brief paroxysms of severe sharp pain. The past treatments included ineffective pharmacological and irreversible surgical approaches. After a comprehensive evaluation, a diagnosis of idiopathic painful nervus intermedius neuropathy was given, which was successfully managed with the use of gabapentin. (3) Conclusions and Practical Implications: The diagnosis and treatment of neuropathic pain affecting the nervus intermedius can be challenging due to the complex nature of the sensory innervation of the ear. The diagnosis can be even more challenging in cases of atypical clinical and demographic presentations, which in turn may result in unsuccessful, unnecessary, and irreversible treatments. Multidisciplinary teams and constant knowledge update are fundamental to provide good quality of care to our patients and to not overlook any relevant signs or symptoms.

Background: Perioperative complications after parotidectomy are poorly studied and have a potential impact on hospitalization stay. The Clavien-Dindo classification of postoperative complications used in visceral surgery allows a recording of all complications, including a grading scale related to the severity of complication. Methods: The cohort analyzed for perioperative complications is composed of 436 parotidectomies. classified into three types, four groups and three classes depending on extent of parotid resection, inclusion of additional procedures and pathology, respectively. Results: Using the Clavien-Dindo classification, complications were reported in 77 % of the interventions. In 438 complications, 430 (98.2%) were classified as minor (332 grade I and 98 grade II) and 8 (1.8%) were classified as major (grade III). Independent variables affecting the risk of perioperative complications were duration of surgery (odds ratio = 1.007, p-value = 0.029) and extent of parotidectomy (odds ratio = 4.043, p-value = 0.007). Total/subtotal parotidectomy was associated with an increased risk of grade II-III complications [odds ratio = 2.866 (95% CI: 1.307-6.283), p-value = 0.009]. Hospital stay increased in patients with complications (p= 0.0064). Conclusions: Use of Clavien-Dindo classification shows that parotidectomy is followed by a high rate of perioperative complications. Longer hospital stay is observed in patients with perioperative complications. Almost all complications are minor and have limited consequence on hospital stay.

Survey data from 42 Australian eastern seaboard veterinary practices involving 506 cases are reported with regard to clinical signs, disease severity, mortality, use of pharmaceuticals, and recovery times. New measures of disease severity (visual analogue scales (VAS) and facial expressions) were tested alongside “gold standard” measures (neuromuscular junction (NMJ) scores). Univariable and multivariable logistic regression analyses were conducted to evaluate associations between variables. The VAS scores were progressive, prognostic (especially the respiratory scores) and correlated with the NMJ scores. The presence of inspiratory dyspnoea and crackles on the day of hospitalization, progressing to expiratory dyspnoea and an expiratory wheeze 24 hours later were highly predictive of mortality. Altered facial features on hospital admission were also highly predictive of mortality. The previously used respiratory score (using various clinical signs) was not predictive of mortality. Older animals had a higher mortality rate and no gender or breed susceptibility was found. The only pharmaceuticals that were positively associated with mortality were tick anti-serum and, in severe cases, antibiotics. The use of many pharmaceutical products (acepromazine, atropine, steroids, antihistamines, antiemetics, diuretics and S8 anti-anxiety and sedation drugs) had no effect on mortality. More drug classes were used with increasing clinical severity and specific factors (e.g. vomiting/retching, hydration) affected the period of hospitalisation. Geographic variation in respiratory signs and toxicity scores was evident, whereas mortality and disease severity were not different across regions.

Patients with excessive gingival display (EGD) are treated with lip repositioning surgeries (LRS). This study used a questionnaire to analyze and evaluate how patients who received LRS with modified and traditional techniques perceived their own oral and facial aesthetics at various timelines after their surgeries. An orofacial esthetic questionnaire (OEQ) was used in this cross-sectional study. The participants were patients who underwent LRS for treatment of their EGD were divided into control (n=100) and test (n=100) groups. For control group, LRS were performed using traditional/conventional technique and for test group, LRS were performed by modified approach. OEQ was used to record responses. The scale comprised of eight questions targeted to capture participating patient’s perceptions about their own oral and facial aesthetics at four timelines (Baseline; 1 Month; 6 Months &amp; 1 Year). Patients responded to each question on a 10 point Likert scale (Range: 0=very dissatisfied to 10= very satisfied). Data was analyzed by independent samples T-tests, using SPSS software. 7 (3.5%) out of the total (200) patients were unable to take part in the study's OEQ. At a one-year follow-up, the mean and standard deviation for the test group's GD were 2.48+0.86mm and 3.77+1.76mm, respectively, and comparisons revealed that the test group's GD was significantly lower (p=0.000) than the control group. Results from the participant responses to OEQ using the Likert scale at the 1-year follow-up revealed significant differences between control and test groups for all questions, except Q5 (P=0.06) as shape of the teeth will not be affected by LRS. Test group patients who underwent the LRS with modified approach were having high level of satisfaction (satisfaction-score&gt;9). Patients in control group scored their satisfaction less favorably, with certain questions (Q3) receiving scores as low as 0.31. The test group's perception of oral and facial esthetics, was significantly higher for test group at different time points. At the 1-year follow-up, the mean difference was 4.46, which was the greatest (P=0.000). The EGD improved significantly at one year with the modified lip repositioning technique. The satisfaction level of the patients with outcomes of modified lip repositioning was significantly higher as compared to conventional technique.

Deep convolutional neural networks (CNNs) have revolutionised computer vision technology by automatically learning hierarchical representations of image data, leading to state-of-the-art performance in visual recognition tasks. This article presents a comprehensive review of deep CNNs, from their evolution and architectures to the current state-of-the-art research. The review also provides the core concepts and building blocks of CNNs and their concise mathematical representations. Furthermore, it explores applications of deep CNNs in three popular domains, including medical diagnosis, remote sensing, and facial recognition. This review will benefit researchers and practitioners in computer vision and artificial intelligence, as well as industry professionals seeking to leverage the latest advancements in deep learning technologies.

The current paradigm shift in orthodontic treatment planning is based on facially driven diagnostics. This requires an affordable, convenient, and non-invasive solution for face scanning. Therefore, utilization of smartphones` TrueDepth sensors is very tempting. TrueDepth refers to front-facing cameras with a dot projector in Apple devices that provide real-time depth data in addition to visual information. There are several applications that tout themselves as accurate solutions for 3D scanning of the face in dentistry. Their clinical accuracy has been uncertain. This study focuses on evaluating the accuracy of the Bellus3D Dental Pro app, which uses Apple's TrueDepth sensor. The app reconstructs a virtual, high-resolution version of the face, which is available for download as a 3D object. In this paper, sixty TrueDepth scans of the face were compared to sixty corresponding facial surfaces segmented from CBCT. Difference maps were created for each pair and evaluated in specific facial regions. The results confirmed statistically significant differences in some facial regions in amplitudes greater than 3 mm, suggesting that current technology has limited applicability for clinical use. The clinical utilization of facial scanning for orthodontic evaluation, which does not require accuracy in the lip region below 3 mm, can be considered.

As the adoption of deep learning models continues to surge across various applications, the need for efficient deployment architectures becomes increasingly critical. This paper presents a novel approach to enhance the deployment of deep learning models by leveraging serverless architecture. Serverless computing has been popular for its auto-scaling, cost-effectiveness, and simplified management characteristics. However, the intense resource demands of deep learning models pose challenges in maintaining low response times and effective load balancing within serverless environments. The proposed architecture addresses these challenges by integrating principles from both deep learning model optimization and serverless computing. Through systematic experimentation and analysis, we demonstrate that by appropriately designing and tuning the deployment architecture, significant improvements in response time, performance, resource utilization, and load distribution can be achieved.

In recent decades, there has been a rising demand for anti-aging interventions aimed at postponing or potentially reversing indicators of skin aging. The use of collagen-based nutraceutical supplements has gained popularity as they have shown promise in enhancing skin health and reducing signs of aging. The aim of this randomized, placebo-controlled, blinded study was to investigate the effects of 2.5 g COLLinstant® LMW, a novel cosmeceutical containing low molecular weight (≤ 1000 Da) collagen peptides, on skin aging and health. The trial was conducted in 80 healthy women aged 30 years and older. They received a daily oral dose of either the food supplement (n = 40) or placebo (n = 40) for six weeks. Skin assessment was performed based on validated objective methods, such as Visioface 1000D (skin wrinkling), cutometry (elasticity and fatigue) and corneometry (skin hydration) at baseline (T0), and at week 6 (T6). After 6 weeks, participants that received collagen had significant improvements in biometric skin wrinkle parameters from baseline, with a reduction in volume by 46%, in area by 44%, and in depth by 9%, along with a greater increase in skin moisturization (by 34%) than those in the placebo group (p &lt; 0.001). The food supplement did not significantly modify skin firmness or fatigue and had only slight beneficial effects on skin elasticity. The investigational product was well tolerated. The observed effects aligned closely with the subjective assessments reported by study participants. The study provides substantiated evidence supporting the efficacy of low molecular weight collagen peptides in restoring altered skin biometric parameters, as objectively assessed. Thus, regular supplementation with this nutraceutical may contribute to achieving smoother and more radiant skin.

Geometrical assessments of human skull have been done based on the anatomical landmarks. Automatic detection of the landmarks, if developed, will be a great help not only medically, but also anthropologically. An automated system with multi-phased deep learning networks to predict three-dimensional coordinate values of cranio-facial landmarks, was developed. From a publicly available database, computed tomography images of craniofacial area were obtained. They were digitally reconstructed into three-dimensional models. Sixteen anatomical landmarks were plotted on each of the models and coordinate values of them were recorded. Three-phased regression deep learning networks were trained respectively with 90 training datasets. For evaluation, 30 testing datasets were employed. Three-dimensional error for the first phase, testing 30 data, was 11.60 pixels in average. (1 pixel = 500 / 512 mm) For the second phase, it was significantly improved to 4.66 pixels. For the third phase, it was significantly progressed to 2.88. This was comparable to the gaps between the landmarks, plotted by two experienced practitioners. Our proposing method of multi-phased prediction, coarse detection first and narrowing down the detection area, may be a possible solution, within the physical limitation of memory and computation.

The aim of this paper was to evaluate the changes in the mean bone density values of the midpalatal suture in 392 young patients treated with the Rapid Palatal Expander appliance according to sex, gender, vertical and sagittal skeletal patterns. Materials and Methods. The evaluations were performed using the low-dose protocol cone-beam computed tomography scans at t0 (preoperatively) and t1 (1 year after the beginning of the therapy). The region of interest was used to calculate bone density in Hounsfield units (HU) in the area between the maxillary incisors. Results. CBCT scan data of 196 females and 196 males (mean age of 11,7 years) showed homogeneous and similar density values of the MPS at T0 (547.59 HU - 565.85 HU) and T1 (542.31 - 554.20 HU). Class III skeletal individuals showed a significant higher BD than the II class group at T0, but not at T1. Females showed significantly higher BD than males at t0 and t1. No significant differences were found between the other groups and between two-time points in terms of bone density values of the MPS. Conclusions. Females and III class groups showed significantly higher bone density values than males and II class, respectively. No statistically significant differences were found from T0 to T1 in any groups, suggesting that a similar rate of suture reorganization occurs after the use of the RPE, following reorganization and bone deposition along with the MPS.

This paper presents preliminary research that investigates the relationship between the flow of a group of jazz musicians, quantified through multi-person pose synchronization, and their collective emotions. Building upon previous studies that measured the physical synchronicity of team members by tracking their body movements and measuring the difference in arm, leg, and head movements, we introduce a novel metric termed "team entanglement". We employ facial expression recognition to evaluate the musicians’ collective emotions. Through correlation and regression analysis, we establish that higher levels of synchronized body and head movements correspond to lower levels of disgust, anger, sadness and higher levels of joy among the musicians. Furthermore, we utilize a Convolutional Neural Network (CNN) based deep learning model to predict the collective emotions of the musicians. This model leverages 17 body synchrony keypoint vectors as features, resulting in a training accuracy of 61.47% and a test accuracy of 66.17%.

In today’s world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. For computer vision currently this is a challenging task to detect normal and abnormal face and facial parts from an input image. In this research paper a method is proposed that can detect normal or abnormal faces from a frontal input image. This method used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation of frequency domain and spatial domain analysis to detect those faces.

This paper proposed emotion recognition methods for consecutive facial images, and implements the inference of neural network model on a field-programmable gate array (FPGA). The proposed emotion recognition methods are based on a neural network model architecture that combines convolution neural networks (CNNs), long short-term memory (LSTM), and fully connected neural networks (FCNNs), called CLDNN or ConvLSTM-FCN. This type of neural network mod-el can analyze the local feature sequences obtained through convolution of data, making it suita-ble for processing time-series data such as consecutive facial images. In this paper, sequences of facial images are sampled from videos corresponding to various emotional state of subjects. The sampled images are then pre-processed with the processes includes facial detection, greyscale conversion, resize, and data augmentation if necessary. The 2-D CNN in ConvLSTM-FCN is used for feature extraction for these pre-processed facial images. These sequences of facial im-age features are time sequences with dependent properties between the elements within them. The LSTM is then used to model these time sequences followed by fully connected neural net-works (FCNNs) for classification. The proposed consecutive facial emotion recognition method achieves an average recognition rate of 99.51% on RAVDESS dataset, 87.80% on BAUM-1s dataset and 96.82% on eNTERFACE’05 data set, using 10-fold cross-validation on the PC. Some com-parisons of recognition accuracies between the proposed method and the other existing related works are conducted in this paper. According to the comparisons, the proposed emotion recog-nition methods outperform the existing related researches. The proposed emotion recognition methods are then implemented on an FPGA chip using the neural network model inference algo-rithms in this paper, and the accuracies of the experiments conducted on the FPGA chip are iden-tical to those obtained on the PC. This verifies that the proposed neural network model imple-mented on the FPGA chip performs well.

Casein hydrolysate improves arterial stiffness, as estimated by brachial ankle pulse wave velocity (baPWV), in untreated hypertensive subjects. Facial pigmentation is a useful biomarker for arterial stiffness. This trial evaluated whether casein hydrolysate improves facial pigmentation in association with changes in arterial stiffness. A randomized, double-blind, placebo-controlled trial was conducted in 80 non-hypertensive Japanese participants randomly assigned to receive either active tablets containing casein hydrolysate or placebo for 48 weeks. Facial pigmentation and baPWV were measured at baseline and at the end of the intervention. Other biochemical atherosclerosis-related parameters were also measured, including advanced glycation end products (AGEs). Changes in facial pigmentation showed a significant difference between the groups. Change in baPWV was significantly better in the active than in the placebo group. In contrast, no significant association was seen between changes in facial pigmentation and those in baPWV. Among other atherosclerosis-related factors, changes in advanced glycation products (AGEs) were significantly decreased in the active compared to the placebo group. Further, changes in facial pigmentation were positively correlated with those in AGEs. Changes in AGEs were independently associated with changes in facial pigmentation. Casein hydrolysate improves facial pigmentation in non-hypertensive participants. Casein hydrolysate may have beneficial effects on glycation stress.

Transparent materials used for facial protection equipment provide protection against microbial infections caused by viruses and bacteria, including multidrug-resistant strains. However, transparent materials used for this type of application are made of materials that do not possess antimicrobial activity. They just avoid direct contact between the person and the biological agent. Therefore, healthy people can get infected through contact of the contaminated material surfaces and this equipment constitute an increasing source of infectious biological waste. Furthermore, infected people can transmit microbial infections easily because the protective equipment do not inactivate the microbial load generated while breathing, sneezing, or coughing. In this regard, the goal of this work consisted of fabricating a transparent face shield with intrinsic antimicrobial activity that could provide extra-protection against infectious agents and reduce the generation of infectious waste. Thus, a single-use transparent antimicrobial face shield composed of polyethylene terephthalate and an antimicrobial coating of benzalkonium chloride has been developed for the next generation of facial protective equipment. The antimicrobial coating was analyzed by atomic force microscopy and field emission scanning electron microscopy with elemental analysis. This is the first facial transparent protective material capable of inactivating enveloped viruses such as SARS-CoV-2 in less than one minute of contact, and the methicillin-resistant Staphylococcus aureus and Staphylococcus epidermidis. Bacterial infections contribute to severe pneumonia associated with the SARS-CoV-2 infection, and their resistance to antibiotics is increasing. Our extra protective broad-spectrum antimicrobial composite material could also be applied for the fabrication of other facial protective tools such as such as goggles, helmets, plastic masks and space separation screens used for counters or vehicles. This low-cost technology would be very useful to combat the current COVID-19 pandemic and protect health care workers from multidrug-resistant infections in developed and underdeveloped countries.

Abstract Background Costello syndrome (CS) is a rare RASopathy caused by a mutation in the proto-oncogene gene HRAS located on chromosome 11p15.5. It is characterized by failure to thrive, intellectual disability, coarse facies, and various cardiovascular, musculoskeletal, and dermatological abnormalities. Aims This literature review describes the various dermatological manifestations of CS in hopes of guiding dermatologists to recognize this rare neurocutaneous syndrome promptly and achieving better patient outcomes. Methods PubMed, MEDLINE, and Scopus were utilized to search for articles and studies on the dermatological features and disorders present in CS. Articles were selected and screened primarily by two reviewers. In the initial article selection process, a third reviewer was included in times of contradiction between the two primary reviewers. Twenty-three articles were ultimately included for analysis. Results CS presents myriad dermatological manifestations, the most characteristic being fine, curly to wavy hair, thick, bushy eyebrows, thin but fast-growing nails, cutaneous papillomas, and loose and redundant skin. Significant cutaneous pathologies associated with CS include eczema, acanthosis nigricans, nevi, coarse facies, and hyperpigmentation. Individuals with CS are also at increased risk for heat intolerance, hyperhidrosis, and photosensitivity. Conclusions Dermatologists must be aware of CS and its dermatological manifestations to efficiently treat the comorbidities involved with this rare neurodevelopmental disorder. CS can present similarly to Cardio-facial-cutaneous syndrome, Noonan syndrome, William syndrome, and Beckwith-Wiedemann syndrome; thus, a keen understanding of the cutaneous differences amongst the syndromes may aid in accurate diagnosis.

The quest for surgical advancements regarding the enhancement of the submental and cervicofacial regions has witnessed a remarkable upsurge in recent years. Informed patients are actively seeking sophisticated plastic surgery techniques to achieve comprehensive rejuvenation in these specific areas. Common complaints expressed by these patients include sagging of the jawline, the emergence of deep perioral wrinkles, and the formation of "marionette lines" within the lower third of the face. Furthermore, the manifestation of age-related signs, including neck laxity, submental adipose accumulation, "witch's chin" deformity, and weakened platysma musculature, are common within this anatomical region. This literature review aims to summarize the recent technical improvements, historical evolution, indications, postoperative care, and challenges for facial rejuvenation of the lower third of the face and neck. Lastly, the application of minimally invasive procedures as part of a comprehensive approach to an aging face will also be discussed.

The quest for surgical advancements regarding the enhancement of the submental and cervicofacial regions has witnessed a remarkable upsurge in recent years. Informed patients are actively seeking sophisticated plastic surgery techniques to achieve comprehensive rejuvenation in these specific areas. Our clinical practice encounters an annual influx of approximately 400 patients, with 20% of them aged over 60, seeking facial rejuvenation encompassing the details of lower third of the face and neck. Common concerns expressed by these patients include sagging of the jawline, the emergence of deep perioral wrinkles, and the formation of "marionette lines" within the lower third of the face. Furthermore, the manifestation of age-related signs, including neck laxity, submental adipose accumulation, "witch's chin" deformity, and weakened platysma musculature, is frequently encountered within this intricate anatomical domain. This review aims to summarize the recent technical improvements, historical evolution, indications, postoperative care and challenges for facial rejuvenation of the lower third of the face and neck. Lastly, the application of minimally invasive procedures as part of a comprehensive approach to an aging face will also be discussed.

Background: The ageing process starts in the center of the face, in the periocular region and around the mouth, with a combination of volume loss and tissue descent, wrinkles deepen, and loss of skin structure and quality. Recently, several studies proved the efficacy of therapies based on the autologous adipose tissue grafting that leverages the properties of stromal vascular fraction (SVF) and adipose derived mesenchymal stem cells (ADSCs) to accelerate the regenerative processes of the skin. This study aimed to verify the ability of the guided Superficial Enhanced Fluid Fat Injection (SEFFI) in the facial area to correct volume loss and skin aging and proved the very low rate of complications of this standardized procedure . Methods: we retrospectively collected data from 2,365 procedures performed in Italian centers between 2019 and 2021. Guided SEFFI was performed alone or combined with cosmetic treatments, including the use of hyaluronic acid filler, suspension threads, synthetic calcium hydroxylapatite, botulin toxin, and microneedling. Results: SEFFI was used alone in more than 60% of the patients, and in all face areas. In about one tenth of patients SEFFI was combined with botulin toxin treatment or hyaluronic acid filling. Other procedures were used more rarely. Ecchymosis in the donor or injection sites was the most frequent event, and yet observed in only 14.2% and 38.6% of patients, respectively. Conclusions: The SEFFI technique is standardized and minimally invasive, leading to very few complications. It can be a promising antiaging medical treatment that combines effectiveness, safety, and simplicity.

A multitude of factors influence our well-being and health in everyday life. Some of which we are totally aware like a delayed train, car exhausts, or continuous work stress. Other factors influence us on a subconscious level, making them, however, not less important to address. One of these factors is the built environment surrounding us almost every second of our lives. The idea of biophilic design states the importance of natural elements implemented in architectural structures to improve the occupants’ health and well-being. Adding to this field of research, this article targets the impact of biophilic design on positive emotions and productivity by using novel approaches like Facial Emotion Recognition (FER) with Residual Masking Networks, and sentiment detection using Large Language Models. Two separate but related studies were conducted and their results reported here. The first study measured emotions of people when confronted with images of different kinds of architecture, via FER (Facial Emotion Recognition) and also via a user survey. We distinguish between self-stated emotions entered through a survey by study participants, and emotions detected from studying the participants’ faces through AI from our FER (Facial Emotion Recognition) system. We found clear trends for most detected emotions, and significant evidence for self-stated emotions, that architecture implementing biophilic design evokes more positive emotions than one that does not. The second study measures the influence of natural elements on productivity and team-engagement. We found that natural elements do influence productivity and sentiment positively; however, only the change in sentiment was shown to be significant. As our sample size, especially for the second study, was relatively small, future research will apply these ideas in a larger setup to acquire further evidence for the importance of biophilic design for human well-being and health.