Integrating Micro-Emotion Recognition with Mental Health Estimation for Improved Well-being

Micro-emotion recognition, through subtle cues like micro-expressions, offers critical insights into an individual’s emotional state, essential for mental health assessment. Micro-expressions, brief and involuntary, reveal hidden emotions but are challenging to detect due to their subtle nature. This research proposes a novel method to capture and analyze micro-expressions for mental health applications by preserving essential facial movements in a single video frame.

The Lateral Accretive Hybrid Network (LEARNet) is introduced to extract micro-level emotional features, refining both broad and subtle facial cues that can indicate mental health conditions like anxiety or depression. Additionally, the authors propose an efficient neural architecture search (NAS) strategy to design a compact, effective CNN for micro-expression recognition, improving performance and resource use.

This approach integrates micro-emotion recognition with mental health estimation, enabling more accurate and early detection of emotional and mental health issues, ultimately leading to improved well-being and mental health monitoring.

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