Classifying Feelings Using Facial Expression Recognition
Keywords:
Facial Expression, Classification, CNN, Attention, Deep Learning, ResNet50.Abstract
This paper challenges the recent methodologies of identify the identical facial expressions in this study we use a dataset containing 48x48 pixel grayscale face images. The importance lies in classifying seven emotions (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral) because their applications in human-computer interaction. We implemented three deep learning models (Regular Convolutional Neural Network (CNN), Attention-Aided CNN, and ResNet50), to evaluate their performance. The results show that the regular deep CNN achieved the highest accuracy at 79% for identifying the identical faces. These findings underscore the potential of networks depth in capturing most of facial expression patterns. This study contributes to the field by illustrating the effectiveness of different models in emotion recognition and provides insights for future research to improve classification accuracy and practical applications.
Downloads
References
Wei Du, "Facial emotion recognition based on improved ResNet," Applied and Computational Engineering, 2023. DOI: 10.54254/2755-2721/21/20231152
Pratyush Shukla, Mahesh Kumar, "Explicating ResNet for Facial Expression Recognition," International Journal of Computing, Intelligent and Communication Technology, 2022. DOI: 10.57061/ijcict.v11i3.3
Marde Fasma’ul Aza, N. Suciati, S. Hidayati, "Performance Study of Facial Expression Recognition Using Convolutional Neural Network," 2020 6th International Conference on Science in Information Technology (ICSITech), 2020. DOI: 10.1109/ICSITech49800.2020.9392070
Narinderpal Kaur, "Facial Expression Recognition Using Convolutional Network," International Journal for Research in Applied Science and Engineering Technology, 2022. DOI: 10.22214/ijraset.2022.44447
Tianyang Zheng, "Experiments on facial emotion recognition based on four different network structures," 2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE), 2022. DOI: 10.1109/ISAIEE57420.2022.00015
Bin Li, Dimas Lima, "Facial expression recognition via ResNet-50," 2021. DOI: 10.1016/J.IJCCE.2021.02.002
Xuejing Ding, V. Mariano, "Research on expression recognition algorithm based on improved convolutional neural network," 2023. DOI: 10.1117/12.2680794
Akash Saravanan, Gurudutt Perichetla, K. Gayathri, "Facial Emotion Recognition using Convolutional Neural Networks," ArXiv, 2019. abs/1910.05602
Ning Zhou, Renyu Liang, Wenqian Shi, "A Lightweight Convolutional Neural Network for Real-Time Facial Expression Detection," IEEE Access, 2021. DOI: 10.1109/ACCESS.2020.3046715
Habib Bahari Khoirullah, N. Yudistira, F. A. Bachtiar, "Facial Expression Recognition Using Convolutional Neural Network with Attention Module," JOIV : International Journal on Informatics Visualization, 2022. DOI: 10.30630/joiv.6.4.963
X. Ding, V. Mariano, "Research on expression recognition algorithm based on improved convolutional neural network," 2023. DOI: 10.1117/12.2680794.
H. B. Khoirullah, N. Yudistira, F. A. Bachtiar, "Facial Expression Recognition Using Convolutional Neural Network with Attention Module," JOIV: International Journal on Informatics Visualization, 2022. DOI: 10.30630/joiv.6.4.963.
S. Mishra et al., "Deep Residual Learning for Facial Emotion Recognition," Mobile Computing and Sustainable Informatics, 2021. DOI: 10.1007/978-981-16-1866-6_22.
P. Shourie, V. Anand, S. Gupta, "Facial Expression Classification using Convolutional Neural Network," 2023 8th International Conference on Communication and Electronics Systems (ICCES), 2023. DOI: 10.1109/ICCES57224.2023.10192618.
C. Liu et al., "Two-Channel Feature Extraction Convolutional Neural Network for Facial Expression Recognition," J. Adv. Comput. Intell. Intell. Informatics, 2020. DOI: 10.20965/jaciii.2020.p0792.
Z. Shi, Z. Tan, "Expression Recognition Method Based on Attention Neural Network," 2021 33rd Chinese Control and Decision Conference (CCDC), 2021. DOI: 10.1109/CCDC52312.2021.9601786.
P. Guo and C. Song, "Facial Expression Recognition with Squeeze-and-Excitation Network," 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), 2022. DOI: 10.1109/ICSP54964.2022.9778358.
J. Li et al., "Attention mechanism-based CNN for facial expression recognition," Neurocomputing, vol. 411, pp. 340-350, 2020. DOI: 10.1016/j.neucom.2020.06.014.
Y. Li, J. Zeng, S. Shan, and X. Chen, "Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism," IEEE Transactions on Image Processing, vol. 28, pp. 2439-2450, 2019. DOI: 10.1109/TIP.2018.2886767.
L. Pham, T. H. Vu, and T. A. Tran, "Facial Expression Recognition Using Residual Masking Network," 2020 25th International Conference on Pattern Recognition (ICPR), 2021. DOI: 10.1109/ICPR48806.2021.9411919.
Y. Ming, H. Qian, and L. Guangyuan, "CNN-LSTM Facial Expression Recognition Method Fused with Two-Layer Attention Mechanism," Computational Intelligence and Neuroscience, 2022. DOI: 10.1155/2022/7450637.
C. Liang, J. Dong, J. Li, J. Meng, Y. Liu, and T. Fang, "Facial expression recognition using LBP and CNN networks integrating attention mechanism," 2023 Asia Symposium on Image Processing (ASIP), 2023. DOI: 10.1109/ASIP58895.2023.00009.
X. Sun, S. Zheng, and H. Fu, "ROI-Attention Vectorized CNN Model for Static Facial Expression Recognition," IEEE Access, vol. 8, pp. 7183-7194, 2020. DOI: 10.1109/ACCESS.2020.2964298.
T. E. Prasad and R. Parvathy, "An Efficient Facial Expression Recognition System Using Novel Image Classification by Comparing CNN over Res Net," Journal of Pharmaceutical Negative Results, 2022. DOI: 10.47750/pnr.2022.13.s04.193.
K. Liu, M. Zhang, and Z. Pan, "Facial Expression Recognition with CNN Ensemble," 2016 International Conference on Cyberworlds (CW), 2016. DOI: 10.1109/CW.2016.34.
G. Shengtao, X. Chao, and F. Bo, "Facial expression recognition based on global and local feature fusion with CNNs," 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2019. DOI: 10.1109/ICSPCC46631.2019.8960765.
Jing Li, Kan Jin, Dalin Zhou, N. Kubota, Zhaojie Ju, "Attention mechanism-based CNN for facial expression recognition," Neurocomputing, 2020. DOI: 10.1016/j.neucom.2020.06.014.
K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. DOI: 10.1109/CVPR.2016.90.
J. Hu, L. Shen, G. Sun, "Squeeze-and-Excitation Networks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. DOI: 10.1109/CVPR.2018.00745.
Y. Li, J. Zeng, S. Shan, X. Chen, "Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism," IEEE Transactions on Image Processing, 2019. DOI: 10.1109/TIP.2018.2886767.
P. Guo, C. Song, "Facial Expression Recognition with Squeeze-and-Excitation Network," 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), 2022. DOI: 10.1109/ICSP54964.2022.9778358.
L. Pham, T. H. Vu, T. A. Tran, "Facial Expression Recognition Using Residual Masking Network," 2020 25th International Conference on Pattern Recognition (ICPR), 2021. DOI: 10.1109/ICPR48806.2021.9411919.
Y. Ming, H. Qian, L. Guangyuan, "CNN-LSTM Facial Expression Recognition Method Fused with Two-Layer Attention Mechanism," Computational Intelligence and Neuroscience, 2022. DOI: 10.1155/2022/7450637.
S. Li, W. Deng, "Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
M. Valstar et al., "AVEC 2017: Real-life Depression, and Affect Recognition Workshop and Challenge," Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, 2017.
J. C. McCall, M. M. Trivedi, "Driver Behavior Recognition and Prediction in a Smart Car," IEEE Transactions on Vehicular Technology, 2007.
El-Gayar, M.M. Automatic Generation of Image Caption Based on Semantic Relation using Deep Visual Attention Prediction. Int. J. Adv. Comput. Sci. Appl. 2023, 14.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
