mujiyantosvc/Facial-Expression-Recognition-FER-for-Mental-Health-Detection-
Facial Expression Recognition (FER) for Mental Health Detection applies AI models like Swin Transformer, CNN, and ViT for detecting emotions linked to anxiety, depression, PTSD, and OCD. It focuses on AI for mental health, emotion detection using OpenCV Python, and real-time applications in healthcare and HR systems.
This project helps healthcare providers, HR professionals, and researchers assess mental well-being by analyzing facial expressions. It takes video or image inputs of faces and identifies emotions like happiness, sadness, or anger, then links these to potential indicators of conditions such as anxiety or depression. The output provides insights into an individual's emotional state, aiding early detection and intervention for mental health concerns.
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Use this if you need an automated, non-invasive method to monitor emotional states and detect early signs of mental health issues from facial expressions in healthcare or workplace settings.
Not ideal if you require a clinical diagnosis or a solution that interprets complex emotional nuances beyond basic expressions and mental health indicators.
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Language
Python
License
MIT
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Last pushed
Jan 30, 2025
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