Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2550
Title: Decoding depressive disorder using computer vision
Authors: Singh J
Goyal G.
Keywords: Depressive disorder
Attention Deficit Hyperactivity Disorder
ConvolutionalNeural Network
DSM-V
Facial emotion recognition
Dlib-ML
Issue Date: 2021
Publisher: Multimedia Tools and Applications
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.
URI: 10.1007/s11042-020-10128-9
http://hdl.handle.net/123456789/2550
Appears in Collections:Journals

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