A computer vision based image processing system for depression detection among students for counseling

Namboodiri Sandhya Parameswaran · D Venkataraman

34 citations0/0 files storedIndonesian Journal of Electrical Engineering and Computer Science

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Emotion and Mood Recognition Face recognition and analysis Face and Expression Recognition

Abstract

Psychological problems in college students like depression, pessimism, eccentricity, anxiety etc. are caused principally due to the neglect of continuous monitoring of students’ psychological well-being. Identification of depression at college level is desirable so that it can be controlled by giving better counseling at the starting stage itself. The disturbed mental state of a student suffering from depression would be clearly evident in the student’s facial expressions.Identification of depression in large group of college students becomes a tedious task for an individual. But advances in the Image-Processing field have led to the development of effective systems, which prove capable of detecting emotions from facial images, in a much simpler way. Thus, we need an automated system that captures facial images of students and analyze them, for effective detection of depression. In the proposed system, an attempt is being made to make use of the Image processing techniques, to study the frontal face features of college students and predict depression. This automated system will be trained with facial features of positive and negative facial emotions. To predict depression, a video of the student is captured, from which the face of the student is extracted. Then using Gabor filters, the facial features are extracted. Classification of these facial features is done using SVM classifier. The level of depression is identified by calculating the amount of negative emotions present in the entire video. Based on the level of depression, notification is send to the class advisor, department counselor or university counselor, indicating the student’s disturbed mental state. The present system works with an accuracy of 64.38%. The paper concludes with the description of an extended architecture for depression detection as future work.

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References

1993Cross-cultural adaptation of health-related quality of life measures: Literature review and proposed guidelines 2009Detecting depression from facial actions and vocal prosody 2013Depression recognition based on dynamic facial and vocal expression features using partial least square regression 2013Social risk and depression: Evidence from manual and automatic facial expression analysis 2017Facial geometry and speech analysis for depression detection 2009Video-based detection of the clinical depression in adolescents 2015Designing a framework for assisting depression severity assessment from facial image analysis 2016Discriminating clinical phases of recovery from major depressive disorder using the dynamics of facial expression

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2020On the review of image and video-based depression detection using machine learning

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