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• W4285114967 abstract "Depression is a mental psychological disorder that may cause a physical disorder or lead to death. It is highly impactful on the social-economical life of a person; therefore, its effective and timely detection is needful. Despite speech and gait, facial expressions have valuable clues to depression. This study proposes a depression detection system based on facial expression analysis. Facial features have been used for depression detection using Support Vector Machine (SVM) and Convolutional Neural Network (CNN). We extracted micro-expressions using Facial Action Coding System (FACS) as Action Units (AUs) correlated with the sad, disgust, and contempt features for depression detection. A CNN-based model is also proposed in this study to auto classify depressed subjects from images or videos in real-time. Experiments have been performed on the dataset obtained from Bahawal Victoria Hospital, Bahawalpur, Pakistan, as per the patient health questionnaire depression scale (PHQ-8); for inferring the mental condition of a patient. The experiments revealed 99.9% validation accuracy on the proposed CNN model, while extracted features obtained 100% accuracy on SVM. Moreover, the results proved the superiority of the reported approach over state-of-the-art methods." @default.
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• W4285114967 date "2022-01-01" @default.
• W4285114967 modified "2023-09-25" @default.
• W4285114967 title "An Automated and Real-time Approach of Depression Detection from Facial Micro-expressions" @default.
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• W4285114967 doi "https://doi.org/10.32604/cmc.2022.028229" @default.
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