EmoSense: AFRN-RAG Framework for FER-Based Facial Emotion Analysis and Mental Health Support

Mental health monitoring is becoming increasingly important with the rising prevalence of psychological disorders, yet traditional assessment methods remain subjective and resource-intensive. This paper presents a hybrid framework that combines an Adaptive Feature Recalibration Network (AFRN) for facial emotion recognition with RetrievalAugmented Generation (RAG) for mental health–related feedback. The AFRN architecture incorporates two key components: adaptive spectral recalibration modules that enhance emotion-relevant features through learned attention mechanisms, and a progressive multi-scale fusion scheme that captures both fine-grained local patterns and global facial configurations. On top of the FER backbone, the RAG component leverages domain-specific psychological resources to generate contextually informed textual summaries and guidance based on detected emotion trajectories. Experimental validation on a standard facial emotion recognition dataset demonstrates that AFRN achieves improved classification performance compared to a baseline CNN, while maintaining computational efficiency suitable for real-time or near real-time applications. The overall EmoSense framework illustrates a feasible pathway from automated emotion detection to psychologically grounded, human-readable feedback, and is positioned as an exploratory tool for AI-assisted mental health monitoring and clinical decision support rather than a standalone diagnostic or therapeutic system.