Emotion Recognition and Depression Detection Using Deep Learning

Computer Engineering

Real-time analysis, labelling, and inference of cognitive affective states from a video capture of the face are made possible by emotion detection systems based on facial gesture. Since it is considered that when an emotion is experienced, facial expressions are temporarily activated, emotion detection can be accomplished by identifying the associated face expression. Depression is one of the six main emotions that are present and is quite important. The term "depression" refers to a mood condition. It might be characterized as despair, rage, or a sense of loss that interferes with daily tasks. Depression manifests itself differently for each person. Depression may sometimes result in deadly situations. To prevent any of them, depression must be identified as soon as possible and the victim must receive the proper care. The project's goal is to use real- time video to analyze a user's emotion. Convolutional neural networks [CNN] are used for this. If the feeling is determined to be depression, it has to be addressed as soon as possible. As the symptoms increase, a person's mental capacity deviates from normal, which results in a disorder. If depression is determined to be the emotion, a Chabot pop-up that was created using the Tkinter library displays on the user's screen asking them to communicate their sentiments. This improves the user's mood while also assessing their level of depression and assisting them in overcoming it. A continuous evaluation is conducted to distinguish between sadness and depression if the user's feeling is determined to be sad.

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