Detection of mental disorders based on the analysis of emotion, facial expressions and facial movements in a video stream

Traditional emotion recognition systems often rely on generalized person-centered models that do not consider the variability of individual emotion expression. This paper explores individual differences in emotion expression and facial expressions for recognizing mental disorders based on video streaming. Using machine learning techniques and deep learning algorithms, we aim to create an algorithm for emotion recognition using a personalized approach. The paper discusses the data collection methods, the condition and the impact of personalization on recognition accuracy. Experimental results demonstrate the advantages of automated personalized models over traditional models, highlighting their potential in the field of affective computing. The study also addresses ethical implications, advocating for bias-mitigated training through cross-cultural datasets and user-controlled calibration. With help of real-time edge computing, our system enables scalable, privacy-preserving mental health monitoring, underscoring the transformative potential of adaptive affective computing and remote diagnostics.

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