Islam, U.; Mahum, R.; AlSalman, A.; Sharaf, M.; Hassan, H.; Huang, B. Facial Emotions Detection using an Efficient Neural Architecture Search Network. Preprints2023, 2023091273. https://doi.org/10.20944/preprints202309.1273.v1
APA Style
Islam, U., Mahum, R., AlSalman, A., Sharaf, M., Hassan, H., & Huang, B. (2023). Facial Emotions Detection using an Efficient Neural Architecture Search Network. Preprints. https://doi.org/10.20944/preprints202309.1273.v1
Chicago/Turabian Style
Islam, U., Haseeb Hassan and Bingding Huang. 2023 "Facial Emotions Detection using an Efficient Neural Architecture Search Network" Preprints. https://doi.org/10.20944/preprints202309.1273.v1
Abstract
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%.
Keywords
Facial Emotion Detection; Deep Learning; Classification; Neural Architecture Search Network
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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