Depression Detection by Facial Emotion Recognition Using Deep Neural Network
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Abstract: The task of facial emotion recognition is one of the widely used applications of image analysis as well as pattern recognition. In the biometric area of research, automatic face & facial expression recognition attracts researcher’s interest. For classifying facial expressions into different categories, it is necessary to extract important facial features that contribute to identifying proper and particular expressions. In this paper, a depression detection system is proposed. This paper will give an overview of the Depression Detection System using Facial Emotion Recognition techniques and datasets. Depression Detection systems based on facial gesture enable real-time analysis, tagging, and inference of cognitive affective states from a video recording of the face. It is assumed that facial expressions are triggered for a time period when an emotion is experienced, and so depression detection can be achieved by detecting the facial expression related to it. Out of all the major 6 emotions present, depression plays a vital role. Depression is classified as a mood disorder. It may be described as feelings of sadness, anger, or loss that interfere with a person’s everyday activities. Experimental results show that the scheme detects depression from real-time video capture from a camera with high accuracy.
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Girard JM, Cohn JF, Mahoor MH, Mavadati S, Rosenwald DP, (2013). Social Risk and Depression: Evidence from Manual and Automatic Facial Expression Analysis: Proc Int Conf Autom Face Gesture Recognit. pp. 1-8. doi: 10.1109/FG.2013.6553748.
Alghowinem, S., Goecke, R., Cohn, JF., Wagner, M., Parker G., and Breakspear, M (2015). Cross-Cultural Detection Of Depression From Nonverbal Behaviour: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, Slovenia, pp. 1-8. doi: 10.1109/FG.2015.7163113.
Katikalapudi, R., Chellappan, S., Montgomery, F., Wunsch D., and Lutzen, K (2012). Associating Internet Usage with Depressive Behavior Among College Students: IEEE Technology and Society Magazine, vol. 31, no. 4, pp. 73-80. doi: 10.1109/MTS.2012.2225462.
Sahla, K.S., Senthil Kumar, T. (2016). Classroom Teaching Assessment Based on Student Emotions: In: Corchado Rodriguez, J., Mitra, S., Thampi, S., El-Alfy, ES. (eds) Intelligent Systems Technologies and Applications 2016. ISTA 2016. Advances in Intelligent Systems and Computing, vol 530. Springer, Cham. doi: 10.1007/978-3-319-47952-1_37
Venkataraman, D., and Parameswaran, NS (2018). Extraction of Facial Features for Depression Detection among Students: International Journal of Pure and Applied Mathematics, Volume 118, No. 7, pp. 455-463.
Parameswaran, NS., and Venkataraman, D., (2019). A Computer Vision Based Image Processing System for Depression Detection Among Students for Counseling: Indonesian Journal of Electrical Engineering and Computer Science, Vol. 14, No. 1, pp. 503-512. doi: 10.11591/ijeecs.v14.i1.pp503-512
Neha, S,. Nivya, Shekar, PHC., Kumar, KS., Asha, VG (2020). Emotion Recognition and Depression Detection using Deep Learning: International Research Journal of Engineering and Technology (IRJET), Vol. 07 No. 08, pp. 3031-3036.
Alghowinem, S., Goecke, R., Wagner, M., Parkerx G., and Breakspear, M. (2013). Head Pose and Movement Analysis as an Indicator of Depression: Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland, pp. 283-288, doi: 10.1109/ACII.2013.53.
Bouhabba, EM., Shafie AA., and Akmeliawati, R., (2011). Support Vector Machine for Face Emotion Detection on Real Time Basis: 4th International Conference on Mechatronics (ICOM), Kuala Lumpur, Malaysia, pp. 1-6, doi: 10.1109/ICOM.2011.5937159.
[10] Cohn JF. et al., (2009). Detecting Depression from Facial Actions and Vocal Prosody: 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, Amsterdam, Netherlands, pp. 1-7, doi: 10.1109/ACII.2009.5349358.
Tasnim, M., Shahriyar, R., Nahar N., and Mahmud, H., (2016). Intelligent Depression Detection and Support System: Statistical Analysis, Psychological Review and Design Implication: IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), Munich, Germany, pp. 1-6. doi: 10.1109/HealthCom.2016.7749494.
Cao,X., Zhai, L., Zhai,P., Li,F., He, T., He,L.(2025): Deep learning-based depression recognition through facial expression: A systematic review: Neurocomputing,vol. 627. doi: 10.1016/j.neucom.2025.129605.
Kumar, G., Das, T. & Singh, K., (2024). Early Detection of Depression Through Facial Expression Recognition and Electroencephalogram-Based Artificial Intelligence-Assisted Graphical User Interface: Neural Comput & Applic, vol. 36, pp. 6937–6954.
Li, M., Wang, Y., Yang, C., Lu Z., and Chen, J. (2024). Automatic Diagnosis of Depression Based on Facial Expression Information and Deep Convolutional Neural Network: IEEE Transactions on Computational Social Systems, vol. 11, no. 5, pp. 5728-5739., doi: 10.1109/TCSS.2024.3393247.
Phiri, D., Makowa, F., Amelia, V., Phiri, Y., Dlamini, L., Chung, M. (2025). Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis. J Med Internet Res, vol. 27, pp. 1-18. doi: 10.2196/59002.
Teferra, B., Rueda, A., Pang, H., Valenzano, R., Samavi, R., Krishnan S., Bhat V. (2024). Screening for Depression Using Natural Language Processing: Literature Review. Interact J Med Res, vol. 13, pp. 1-17. doi: 10.2196/55067.
Saha, D.K., Hossain, T., Safran, M. et al. (2024). Ensemble of Hybrid Model Based Technique for Early Detecting of Depression Based on SVM And Neural Networks. Sci Rep, vol. 14, pp.1-18. doi: 10.1038/s41598-024-77193-0.
Kaggle Data set: https://www.kaggle.com/datasets
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