Version 1
: Received: 20 October 2023 / Approved: 23 October 2023 / Online: 23 October 2023 (10:29:47 CEST)
How to cite:
Sousa, L.; Canedo, D.; Drummond, M.; Ferreira, J.; Neves, A. A Data-Centric Approach for Detecting a Neutral Facial Expression using Deep Learning. Preprints2023, 2023101433. https://doi.org/10.20944/preprints202310.1433.v1
Sousa, L.; Canedo, D.; Drummond, M.; Ferreira, J.; Neves, A. A Data-Centric Approach for Detecting a Neutral Facial Expression using Deep Learning. Preprints 2023, 2023101433. https://doi.org/10.20944/preprints202310.1433.v1
Sousa, L.; Canedo, D.; Drummond, M.; Ferreira, J.; Neves, A. A Data-Centric Approach for Detecting a Neutral Facial Expression using Deep Learning. Preprints2023, 2023101433. https://doi.org/10.20944/preprints202310.1433.v1
APA Style
Sousa, L., Canedo, D., Drummond, M., Ferreira, J., & Neves, A. (2023). A Data-Centric Approach for Detecting a Neutral Facial Expression using Deep Learning. Preprints. https://doi.org/10.20944/preprints202310.1433.v1
Chicago/Turabian Style
Sousa, L., João Ferreira and António Neves. 2023 "A Data-Centric Approach for Detecting a Neutral Facial Expression using Deep Learning" Preprints. https://doi.org/10.20944/preprints202310.1433.v1
Abstract
Neutral facial expression recognition is of great importance in various domains and applications. This study introduces a data-centric approach for neutral facial expression recognition, presenting a comprehensive study that explores different methodologies, techniques, and challenges in the field to foster a deeper understanding. The results show that data augmentation plays a crucial role in improving dataset performance. Additionally, the study investigates different model architectures and training techniques to identify the most effective approach, with the InceptionV3 model achieving the highest accuracy of 72%. Furthermore, the research examines the influence of preprocessing methods on the performance of both InceptionV3 and a simplified CNN model. Interestingly, the results indicate that preprocessing techniques positively affect the performance of the simpler CNN model but negatively impact the InceptionV3 model. The implemented system, used to evaluate the findings, demonstrates promising results, correctly classifying 77% of neutral expressions. However, there are still areas for improvement. Creating a specialized dataset that includes both neutral and non-neutral expressions would greatly enhance the accuracy of the system. By addressing limitations and implementing suggested improvements, neutral facial expression recognition can be significantly enhanced, leading to more effective and accurate results.
Keywords
deep learning; emotion classification; machine learning; neutral emotion classification
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.
