Facial Emotion Recognition Based on Deep Learning: A Review
Keywords:
Facial Emotion Recognition, Deep Learning, Emotion, Human Computer Interaction
Abstract
Numerous domains, including safety, health, and human-machine interfaces, have garnered significant attention from researchers. Within this field, there is a notable interest in developing methodologies for interpreting and encoding facial expressions, as well as extracting pertinent features for more accurate computer-based predictions. Leveraging the remarkable advancements in deep learning, various architectural approaches are explored to enhance performance outcomes. The primary objective of this paper is to conduct an examination of recent research endeavors pertaining to automatic facial emotion recognition (FER) through the utilization of deep learning techniques. We emphasize the treatment of these contributions, elucidate the architectural frameworks employed, and outline the databases that have been utilized. Additionally, we present a comprehensive assessment of the progress achieved by comparing the methodologies proposed and the corresponding results obtained. This paper aims to provide valuable insights and guidance to researchers in this field by reviewing recent developments and suggesting avenues for further enhancements
References
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[33] Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2017). Affectnet: A database for facial expression, valence, and arousal computing in the wild. IEEE Transactions on Affective Computing, 10(1), 18-31.
[34] Koelstra, S., Muhl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., ... & Patras, I. (2011). Deap: A database for emotion analysis; using physiological signals. IEEE transactions on affective computing, 3(1), 18-31.
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[36] Calvo, M. G., Fernández-Martín, A., Recio, G., & Lundqvist, D. (2018). Human observers and automated assessment of dynamic emotional facial expressions: KDEF-dyn database validation. Frontiers in psychology, 9, 2052.
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[43] Guo, J. (2022). Deep learning approach to text analysis for human emotion detection from big data. Journal of Intelligent Systems, 31(1), 113-126.
[44] Kim, D. H., Baddar, W. J., Jang, J., & Ro, Y. M. (2017). Multi-objective based spatio-temporal feature representation learning robust to expression intensity variations for facial expression recognition. IEEE Transactions on Affective Computing, 10(2), 223-236.
[45] Li, Y., Zeng, J., Shan, S., & Chen, X. (2018). Occlusion aware facial expression recognition using CNN with attention mechanism. IEEE Transactions on Image Processing, 28(5), 2439-2450.
[46] Zou, J., Cao, X., Zhang, S., & Ge, B. (2019, April). A facial expression recognition based on improved convolutional neural network. In 2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE) (pp. 301-304). IEEE.
[47] Agrawal, A., & Mittal, N. (2020). Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy. The Visual Computer, 36(2), 405-412.
[48] Liang, D., Liang, H., Yu, Z., & Zhang, Y. (2020). Deep convolutional BiLSTM fusion network for facial expression recognition. The Visual Computer, 36, 499-508.
[49] Li, J., Jin, K., Zhou, D., Kubota, N., & Ju, Z. (2020). Attention mechanism-based CNN for facial expression recognition. Neurocomputing, 411, 340-350.
[50] Lu, X. (2022). Deep learning based emotion recognition and visualization of figural representation. Frontiers in psychology, 12, 818833.
[51] Bhatti, Y. K., Jamil, A., Nida, N., Yousaf, M. H., Viriri, S., & Velastin, S. A. (2021). Facial expression recognition of instructor using deep features and extreme learning machine. Computational Intelligence and Neuroscience, 2021, 1-17.
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[58] Jia, X., Xu, S., Zhou, Y., Wang, L., & Li, W. (2023). A novel dual-channel graph convolutional neural network for facial action unit recognition. Pattern Recognition Letters, 166, 61-68.
[59] Sarvakar, K., Senkamalavalli, R., Raghavendra, S., Kumar, J. S., Manjunath, R., & Jaiswal, S. (2023). Facial emotion recognition using convolutional neural networks. Materials Today: Proceedings, 80, 3560-3564.
[60] Chowdary, M. K., Nguyen, T. N., & Hemanth, D. J. (2023). Deep learning-based facial emotion recognition for human–computer interaction applications. Neural Computing and Applications, 35(32), 23311-23328
[2] Jain, D. K., Shamsolmoali, P., & Sehdev, P. (2019). Extended deep neural network for facial emotion recognition. Pattern Recognition Letters, 120, 69-74.
[3] Ansari, S., Kulkarni, P., Rajesh, T., & Gurudas, V. R. (2023, April). Facial Emotion Detection Using Deep Learning: A Survey. In 2023 IEEE International Conference on Contemporary Computing and Communications (InC4) (Vol. 1, pp. 1-4). IEEE.
[4] Chaudhari, A., Bhatt, C., Nguyen, T. T., Patel, N., Chavda, K., & Sarda, K. (2023). Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques. SN Computer Science, 4(4), 363.
[5] Hassouneh, A., Mutawa, A. M., & Murugappan, M. (2020). Development of a real-time emotion recognition system using facial expressions and EEG based on machine learning and deep neural network methods. Informatics in Medicine Unlocked, 20, 100372.
[6] Pranav, E., Kamal, S., Chandran, C. S., & Supriya, M. H. (2020, March). Facial emotion recognition using deep convolutional neural network. In 2020 6th International conference on advanced computing and communication Systems (ICACCS) (pp. 317-320). IEEE.
[7] Zulkarnain, S. T., & Suciati, N. (2022). Selective local binary pattern with convolutional neural network for facial expression recognition. International Journal of Electrical & Computer Engineering (2088-8708), 12(6).
[8] Xie, W., Jia, X., Shen, L., & Yang, M. (2019). Sparse deep feature learning for facial expression recognition. Pattern Recognition, 96, 106966.
[9] Sikkandar, H., & Thiyagarajan, R. (2021). Deep learning based facial expression recognition using improved Cat Swarm Optimization. Journal of Ambient Intelligence and Humanized Computing, 12, 3037-3053.
[10] Cîrneanu, A. L., Popescu, D., & Iordache, D. (2023). New trends in emotion recognition using image analysis by neural networks, a systematic review. Sensors, 23(16), 7092.
[11] Prentice, C. (2023). Leveraging Emotional and Artificial Intelligence for Organisational Performance. Springer Nature.
[12] Adyapady, R. R., & Annappa, B. (2023). A comprehensive review of facial expression recognition techniques. Multimedia Systems, 29(1), 73-103.
[13] Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., & Taylor, J. G. (2001). Emotion recognition in human-computer interaction. IEEE Signal processing magazine, 18(1), 32-80.
[14] Utegen, D., & Rakhmetov, B. Z. (2023). Facial Recognition Technology and Ensuring Security of Biometric Data: Comparative Analysis of Legal Regulation Models. Journal of Digital Technologies and Law, 1(3), 825-844.
[15] Abdullah, S. M. S., & Abdulazeez, A. M. (2021). Facial expression recognition based on deep learning convolution neural network: A review. Journal of Soft Computing and Data Mining, 2(1), 53-65.
[16] Kako, N. A., & Abdulazeez, A. M. (2022). Peripapillary Atrophy Segmentation and Classification Methodologies for Glaucoma Image Detection: A Review. Current Medical Imaging, 18(11), 1140-1159.
[17] Liu, C., Hirota, K., & Dai, Y. (2023). Patch attention convolutional vision transformer for facial expression recognition with occlusion. Information Sciences, 619, 781-794.
[18] Benrouba, F., & Boudour, R. (2023). Emotional sentiment analysis of social media content for mental health safety. Social Network Analysis and Mining, 13(1), 17.
[19] Mukhiddinov, M., Djuraev, O., Akhmedov, F., Mukhamadiyev, A., & Cho, J. (2023). Masked Face Emotion Recognition Based on Facial Landmarks and Deep Learning Approaches for Visually Impaired People. Sensors, 23(3), 1080.
[20] Win, S. S. K., Siritanawan, P., & Kotani, K. (2023). Compound facial expressions image generation for complex emotions. Multimedia Tools and Applications, 82(8), 11549-11588.
[21] Jung, Y., & Wheeler, A. P. (2023). The effect of public surveillance cameras on crime clearance rates. Journal of Experimental Criminology, 19(1), 143-164.
[22] Yu, Y., Niu, Q., Li, X., Xue, J., Liu, W., & Lin, D. (2023). A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications. Micromachines, 14(6), 1253.
[23] Kako, N. A., Abdulazeez, A. M., & Sadeeq, H. T. (2021, February). Effect of Colored Noise on Neuron Membrane Size Using Stochastic Hodgkin-Huxley Equations. In 2021 7th International Engineering Conference “Research & Innovation amid Global Pandemic"(IEC) (pp. 20-25). IEEE.
[24] Kako, N. A. (2013). An Investigation of the Coefficient of Variation Using the Colored Stochastic Hodgkin-Huxley Equations (Doctoral dissertation, Eastern Mediterranean University (EMU)-Doğu Akdeniz Üniversitesi (DAÜ)).
[25] Kako, N. A., Sadeeq, H. T., & Abrahim, A. R. (2020). New symmetric key cipher capable of digraph to single letter conversion utilizing binary system. Indonesian Journal of Electrical Engineering and Computer Science, 18(2), 1028.
[26] Kako, N. A. (2018). Classical Cryptography for Kurdish Language. In 4th International Engineering Conference on Developments in Civil & Computer Engineering Applications (IEC2018) (pp. 20-28).
[27] Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010, June). The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In 2010 ieee computer society conference on computer vision and pattern recognition-workshops (pp. 94-101). IEEE.
[28] Lyons, M. J., Kamachi, M., & Gyoba, J. (2020). Coding facial expressions with Gabor wavelets (IVC special issue). arXiv preprint arXiv:2009.05938.
[29] Goodfellow, I. J., Erhan, D., Carrier, P. L., Courville, A., Mirza, M., Hamner, B., ... & Bengio, Y. (2013). Challenges in representation learning: A report on three machine learning contests. In Neural Information Processing: 20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. Proceedings, Part III 20 (pp. 117-124). Springer berlin heidelberg.
[30] Gross, R., Matthews, I., Cohn, J., Kanade, T., & Baker, S. (2010). Multi-pie. Image and vision computing, 28(5), 807-813.
[31] Yan, W. J., Li, X., Wang, S. J., Zhao, G., Liu, Y. J., Chen, Y. H., & Fu, X. (2014). CASME II: An improved spontaneous micro-expression database and the baseline evaluation. PloS one, 9(1), e86041.
[32] T. Dias, J. Vitorino, J. Oliveira, N. Oliveira, E. Maia, and I. Praça, “IFEED: Interactive Facial Expression and Emotion Detection Dataset.” Zenodo, May 2023. doi: 10.5281/zenodo.7963452.
[33] Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2017). Affectnet: A database for facial expression, valence, and arousal computing in the wild. IEEE Transactions on Affective Computing, 10(1), 18-31.
[34] Koelstra, S., Muhl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., ... & Patras, I. (2011). Deap: A database for emotion analysis; using physiological signals. IEEE transactions on affective computing, 3(1), 18-31.
[35] Jalal, A., & Tariq, U. (2017). The LFW-gender dataset. In Computer Vision–ACCV 2016 Workshops: ACCV 2016 International Workshops, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III 13 (pp. 531-540). Springer International Publishing.
[36] Calvo, M. G., Fernández-Martín, A., Recio, G., & Lundqvist, D. (2018). Human observers and automated assessment of dynamic emotional facial expressions: KDEF-dyn database validation. Frontiers in psychology, 9, 2052.
[37] Juneja, K., & Rana, C. (2021). An extensive study on traditional-to-recent transformation on face recognition system. Wireless Personal Communications, 118, 3075-3128.
[38] Ali, N. N., Kako, N. A., & Abdi, A. S. (2022, September). Review on Image Segmentation Methods Using Deep Learning. In 2022 4th International Conference on Advanced Science and Engineering (ICOASE) (pp. 7-12). IEEE.
[39] Sulaiman, D. M., Abdulazeez, A. M., Zebari, D. A., Zeebaree, D. Q., Mostafa, S. A., & Sadiq, S. S. (2022). An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification. Traitement du Signal, 39(6), 1991.
[40] Sajjad, M., Ullah, F. U. M., Ullah, M., Christodoulou, G., Cheikh, F. A., Hijji, M., ... & Rodrigues, J. J. (2023). A comprehensive survey on deep facial expression recognition: challenges, applications, and future guidelines. Alexandria Engineering Journal, 68, 817-840.
[41] Li, S., & Deng, W. (2020). Deep facial expression recognition: A survey. IEEE transactions on affective computing, 13(3), 1195-1215.
[42] Jaiswal, A., Raju, A. K., & Deb, S. (2020, June). Facial emotion detection using deep learning. In 2020 international conference for emerging technology (INCET) (pp. 1-5). IEEE.
[43] Guo, J. (2022). Deep learning approach to text analysis for human emotion detection from big data. Journal of Intelligent Systems, 31(1), 113-126.
[44] Kim, D. H., Baddar, W. J., Jang, J., & Ro, Y. M. (2017). Multi-objective based spatio-temporal feature representation learning robust to expression intensity variations for facial expression recognition. IEEE Transactions on Affective Computing, 10(2), 223-236.
[45] Li, Y., Zeng, J., Shan, S., & Chen, X. (2018). Occlusion aware facial expression recognition using CNN with attention mechanism. IEEE Transactions on Image Processing, 28(5), 2439-2450.
[46] Zou, J., Cao, X., Zhang, S., & Ge, B. (2019, April). A facial expression recognition based on improved convolutional neural network. In 2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE) (pp. 301-304). IEEE.
[47] Agrawal, A., & Mittal, N. (2020). Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy. The Visual Computer, 36(2), 405-412.
[48] Liang, D., Liang, H., Yu, Z., & Zhang, Y. (2020). Deep convolutional BiLSTM fusion network for facial expression recognition. The Visual Computer, 36, 499-508.
[49] Li, J., Jin, K., Zhou, D., Kubota, N., & Ju, Z. (2020). Attention mechanism-based CNN for facial expression recognition. Neurocomputing, 411, 340-350.
[50] Lu, X. (2022). Deep learning based emotion recognition and visualization of figural representation. Frontiers in psychology, 12, 818833.
[51] Bhatti, Y. K., Jamil, A., Nida, N., Yousaf, M. H., Viriri, S., & Velastin, S. A. (2021). Facial expression recognition of instructor using deep features and extreme learning machine. Computational Intelligence and Neuroscience, 2021, 1-17.
[52] Gill, R., & Singh, J. (2021, December). A deep learning approach for real time facial emotion recognition. In 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 497-501). IEEE.
[53] Zebari, G. M., Zebari, D. A., Zeebaree, D. Q., Haron, H., Abdulazeez, A. M., & Yurtkan, K. (2021, December). Efficient CNN Approach for Facial Expression Recognition. In Journal of Physics: Conference Series (Vol. 2129, No. 1, p. 012083). IOP Publishing.
[54] Umer, S., Rout, R. K., Pero, C., & Nappi, M. (2022). Facial expression recognition with trade-offs between data augmentation and deep learning features. Journal of Ambient Intelligence and Humanized Computing, 1-15.
[55] Saeed, J. N., Abdulazeez, A. M., & Ibrahim, D. A. (2022, September). FIAC-Net: Facial image attractiveness classification based on light deep convolutional neural network. In 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA) (pp. 1-6). IEEE.
[56] Saeed, J. N., Abdulazeez, A. M., & Ibrahim, D. A. (2022, September). 2D Facial Images Attractiveness Assessment Based on Transfer Learning of Deep Convolutional Neural Networks. In 2022 4th International Conference on Advanced Science and Engineering (ICOASE) (pp. 13-18). IEEE.
[57] Saeed, J. N., Abdulazeez, A. M., & Ibrahim, D. A. (2023). An Ensemble DCNNs-Based Regression Model for Automatic Facial Beauty Prediction and Analyzation. Traitement du Signal, 40(1), 55.
[58] Jia, X., Xu, S., Zhou, Y., Wang, L., & Li, W. (2023). A novel dual-channel graph convolutional neural network for facial action unit recognition. Pattern Recognition Letters, 166, 61-68.
[59] Sarvakar, K., Senkamalavalli, R., Raghavendra, S., Kumar, J. S., Manjunath, R., & Jaiswal, S. (2023). Facial emotion recognition using convolutional neural networks. Materials Today: Proceedings, 80, 3560-3564.
[60] Chowdary, M. K., Nguyen, T. N., & Hemanth, D. J. (2023). Deep learning-based facial emotion recognition for human–computer interaction applications. Neural Computing and Applications, 35(32), 23311-23328
Published
2024-05-07
How to Cite
Ali, N., & Abdulazeez, A. (2024). Facial Emotion Recognition Based on Deep Learning: A Review. International Journal of Research and Applied Technology (INJURATECH), 4(1), 21-34. Retrieved from https://ojs.unikom.ac.id/index.php/injuratech/article/view/13619
Section
Articles
