A Review on Depression Detection Among Adolescent by Face


  • Ravi Kumar M.Tech. Scholar Department of CSE NIIST, Bhopal, India
  • Santosh Kumar Nagar Assistant Professor Department of CSE NIIST, Bhopal, India
  • Anurag Shrivastava Associate Professor Department of CSE NIIST, Bhopal, India




Depression detection, Facial Features, Facial Emotions


Depression has become one of the most common mental illnesses in the past decade, affecting millions of patients and their families. However, the methods of diagnosing depression almost exclusively rely on questionnaire-based interviews and clinical judgments of symptom severity, which are highly dependent on doctors’ experience and makes it a labor-intensive work. Our study aims to develop an objective and convenient method to assist depression detection using facial features in adolescent. Most of the adolescent are totally unaware that they may be having depression. If at all they are aware of it, some adolescents conceal their depression from everyone. So, an automated system is required that will pick out the adolescents who are dealing with depression. In this paper, different research work focused for detecting depression are discussed.


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Beck, Aaron T. Depression: Clinical, experimental, and theoretical aspects. University of Pennsylvania Press, 1967.
Persons, Jacqueline B. ”Psychotherapy outcome studies do not accurately represent current models of psychotherapy: A proposed remedy.” American psychologist 46.2, 1991.
Clarke, Greg, et al. ”Overcoming Depression on the Internet (ODIN)(2): a randomized trial of a self-help depression skills program with reminders.” Journal of medical Internet research 7.2, 2005.
Gilson, Mark,and Arthur Freeman. Overcoming depression: A cognitive therapy approach therapist guide. Oxford University Press, 2009.
Plutchik, Robert. The emotions. University Press of America, 1991.
Videbeck, Sheila, and Sheila Videbeck. Psychiatric-mental health nursing. Lippincott Williams & Wilkins, 2013.
Singh, Harman, Neeti Dhanak, Haroon Ansari, and Krishan Kumar. ”HDML: Habit Detectionwith Machine Learning.” In Proceedings of the 7th International Conference on Computer and Communication Technology, pp. 29-33.ACM, 2017.
Cohn, Jeffrey F., et al. ”Detecting depression from facial actions and vocal prosody.” Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on. IEEE, 2009.
Sharma, Shikhar, et al., ”D-FES:Deep facial expression recognition system.”Information and Communication Technology (CICT), 2017 Conference on. IEEE, 2017.
S.Shikhar, et al., ”LEXER: LEXicon Based Emotion AnalyzeR.” International Conference on Pattern Recognition and Machine Intelligence. Springer, Cham, 2017.
Low, Lu-Shih Alex, et al. ”Content based clinical depression detection in adolescents.” Signal Processing Conference, 2009 17th European. IEEE, 2009.
Low, Lu-Shih Alex, et al. ”Detection of clinical depression in adolescents speech during family interactions.” IEEE Transactions on Biomedical Engineering 58.3, 2011.
Wang, Xinyu, et al. ”A depression detection model based on sentiment analysis in micro-blog social network.” Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2013.
Wang, Xinyu, Chunhong Zhang, and Li Sun. ”An improved model for depression detection inmicro-blog social network.” 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE, 2013.
Shen, Tiancheng,“CrossDomain Depression Detection via Harvesting Social Media.” Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. 2018.
T. Baltrusaitis et al., “OpenFace: An Open Source Facial Behavior ? Analysis Toolkit,” in IEEE-WACV, 2016, pp. 1–10.
V. Vonikakis et al., “Group happiness assessment using geometric features and dataset balancing,” in 18th ACM-ICMI. ACM, 2016, pp. 479–486.[18]S. Alghowinem et al., “Eye movement analysis for depression detection,” in 20th IEEE-ICIP, 2013, pp. 4220–4224.
M. Valstar et al., “AVEC 2014: 3D Dimensional Affect and DepressionRecognition Challenge,” in 4th ACM-AVEC, 2014, pp. 3–10.
Pampouchidou, Anastasia,“Depression Assessment by FusingHigh and Low Level Features from Audio, Video, and Text.” Proceedingsof the 6th International Workshop on Audio/Visual Emotion Challenge.ACM, 2016.




How to Cite

Kumar, R., Nagar, S. K., & Shrivastava, A. (2020). A Review on Depression Detection Among Adolescent by Face. SMART MOVES JOURNAL IJOSCIENCE, 6(1), 32–35. https://doi.org/10.24113/ijoscience.v6i1.257