<![CDATA[Depression is a mental illness that usually goes untreated in people and can have catastrophic consequences, including suicidal thoughts. Counselling services are widely available, but because depression is a stigmatized illness, many people who are depressed decide not to seek help. Therefore, it is essential to develop an automated system that can recognize depression in individuals before it worsens. In this study, a novel approach is proposed for identifying depression using a combination of visual and vocal emotions. Long short-term memory (LSTM) is used to assess verbal expressions and convolutional neural networks (CNN) to analyze facial expressions. The proposed system is trained using features of depression from the distress analysis interview corpus (DAIC) dataset and tested on videos of college students with frontal faces. The proposed approach is effective in detecting depression in individuals, with high accuracy and reliability.]]>
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