A Comprehensive Review on Depression Detection using Computer Intelligence


  • Hema Mahawar M.Tech Scholar, Department of CSE, Radharaman Institute Of Technology & Science, Bhopal, M.P, India
  • Ravi Verma Assistant Professor, Department of CSE, Radharaman Institute Of Technology & Science, Bhopal, M.P, India
  • Chetan Agrawal Assistant Professor, Department of CSE Radharaman, Institute Of Technology & Science, Bhopal, M.P, India




Depression Detection, Facial Features, Facial Emotions


The depression that has affected millions of patients and their families in the last decade has been one of the most common diseases of mind. However, almost all methods of medicating anxiety depend on series of questions evaluations and professional judgments of depressive symptoms, which are highly reliant on doctors’ experience and time-consuming. The aim of our research is to create an objective and practical method for detecting depression in adolescents using facial characteristics. The majority of teenagers are wholly unaware that they have been depressed. Some teenagers hide their depression from others, whether they are even aware of it. An automated system is needed to select the teenagers who face depression. Various research projects aimed at identifying depression are explored in this article.


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Beck, Aaron T. Depression: Clinical, experimental, and theoretical aspects. University of Pennsylvania Press, 1967.

Plutchik, Robert. The emotions. University Press of America, 1991.

Persons, Jacqueline B. ”Psychotherapy outcome studies do not accurately represent current models of psychotherapy: A proposed remedy.” American psychologist 46.2, 1991. DOI: https://doi.org/10.1037/0003-066X.46.2.99

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. DOI: https://doi.org/10.2196/jmir.7.2.e16

Gilson, Mark, and Arthur Freeman. Overcoming depression: A cognitive therapy approach therapist guide. Oxford University Press, 2009. DOI: https://doi.org/10.1093/med:psych/9780195300000.001.0001

Low, Lu-Shih Alex, et al. ”Content based clinical depression detection in adolescents.” Signal Processing Conference, 2009 17th European. IEEE, 2009.

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. DOI: https://doi.org/10.1109/ACII.2009.5349358

Low, Lu-Shih Alex, et al. ”Detection of clinical depression in adolescents speech during family interactions” IEEE Transactions on Biomedical Engineering 58.3, 2011. DOI: https://doi.org/10.1109/TBME.2010.2091640

Videbeck, Sheila, and Sheila Videbeck. Psychiatric-mental health nursing. Lippincott Williams & Wilkins, 2013.

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. DOI: https://doi.org/10.1007/978-3-642-40319-4_18

Wang, Xinyu, Chunhong Zhang, and Li Sun. ”An improved model for depression detection in micro-blog social network.” 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE, 2013. DOI: https://doi.org/10.1109/ICDMW.2013.132

T. Baltrusaitis et al., “OpenFace: An Open Source Facial Behavior ? Analysis Toolkit,” in IEEE-WACV, 2016, pp. 1–10. DOI: https://doi.org/10.1109/WACV.2016.7477553

S. Alghowinem et al., “Eye movement analysis for depression detection,” in 20th IEEE-ICIP, 2013, pp. 4220–4224. DOI: https://doi.org/10.1109/ICIP.2013.6738869

M. Valstar et al., “AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge,” in 4th ACM-AVEC, 2014, pp. 3–10. DOI: https://doi.org/10.1145/2661806.2661807

Pampouchidou, Anastasia, “Depression Assessment by Fusing High and Low Level Features from Audio, Video, and Text.” Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge. ACM, 2016. DOI: https://doi.org/10.1145/2988257.2988266

V. Vonikakis et al., “Group happiness assessment using geometric features and dataset balancing,” in 18th ACM-ICMI. ACM, 2016, pp. 479–486. DOI: https://doi.org/10.1145/2993148.2997633

Sharma, Shikhar, et al., ”D-FES: Deep facial expression recognition system.” Information and Communication Technology (CICT), 2017 Conference on. IEEE, 2017. DOI: https://doi.org/10.1109/INFOCOMTECH.2017.8340635

Singh, Harman, Neeti Dhanak, Haroon Ansari, and Krishan Kumar. ”HDML: Habit Detection with Machine Learning.” In Proceedings of the 7th International Conference on Computer and Communication Technology, pp. 29-33. ACM, 2017. DOI: https://doi.org/10.1145/3154979.3154996

S. Shikhar, et al., ”LEXER: LEXicon Based Emotion AnalyzeR.” International Conference on Pattern Recognition and Machine Intelligence. Springer, Cham, 2017.

Shen, Tiancheng, “Cross Domain Depression Detection via Harvesting Social Media.” Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. 2018. DOI: https://doi.org/10.24963/ijcai.2018/223




How to Cite

Mahawar, H. ., Verma, R. ., & Agrawal, C. . (2020). A Comprehensive Review on Depression Detection using Computer Intelligence. SMART MOVES JOURNAL IJOSCIENCE, 6(7), 36–39. https://doi.org/10.24113/ijoscience.v6i7.359