Decoding depressive disorder using computer vision
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Decoding depressive disorder using computer vision Jaiteg Singh1 · Gaurav Goyal1 Received: 16 May 2020 / Revised: 2 September 2020 / Accepted: 19 October 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper intends to decode depressive disorder using computer vision. Facial expressions rendered by a depressive and non-depressive person were studied against a given stimulus. A survey was conducted using Attention Deficit Hyperactivity Disorder (ADHD) questionnaire on a group of four hundred one volunteers between age group of nineteen to twenty-three years. A total of 254 male and 147 female volunteers participated in survey. Three hundred and eighty-seven responses were actually received and seventy-two respondents were identified as potential patients of depressive disorder. Amongst these anticipated depressive patients, thirty-eight were called for a personal assessment/ interview by practicing psychologists as per DSM-V standards. Data collected from these respondents were used to train a Convolutional Neural Network model, so as to classify a person as depressed or not depressed. The proposed system attained a precision of 74 in the identification of depressive patients. This study concludes to the fact that facial expressions rendered by a patient suffering from the depressive disorder are different from that of non-depressive person against any given psychological stimulus. Further, it was also concluded that facial expressions rendered by a respondent against any annotated quality stimulus like ADFES dataset could provide results comparable to that of ADHD questionnaire. The outcome of this research intends to facilitate doctors to identify potential depressive patients and make an early diagnosis. Keywords Depressive disorder · Attention Deficit Hyperactivity Disorder · Convolutional Neural Network · DSM-V · Facial emotion recognition · Dlib-ML
1 Introduction Survey of Health, Ageing, and Retirement in Europe have reported a substantial rise in the count of aging depressive adults. Further, a significantly high risk of suicidal behaviour was said to be associated with depression. The dynamic global economy and intense competition for employment opportunities have resulted in a considerable rise in the display of depressive symptomatology. Anxiety and depression have affected nearly twenty present Jaiteg Singh
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Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, India
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of the world population [1, 2]. Anxiety and depression being internalizing disorders can even impair the socio-emotional development of an individual [3, 4]. Such internalizing factors may lead to health and social problems like psychopathology, suicides, drug abuse and even functional impairment [5, 6, 9, 11]. A depressive disorder is also integral to many impairments related to social functioning, which may persist even during remission [12, 13, 20]. Depression is a leading cause of ill health and d
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