Exploiting facial emotion recognition system for ambient assisted living technologies triggered by interpreting the user's emotional state.

Introduction: Facial Emotion Recognition (FER) enables smart environments and robots to adapt their behavior to a user's affective state. Translating those recognized emotions into ambient cues, such as colored lighting, can improve comfort and engagement in Ambient Assisted Living (AAL) settings. Methods: We design a FER pipeline that combines a Spatial Transformer Network for pose-invariant region focusing with a novel Multiple Self-Attention (MSA) block comprising parallel attention heads and learned fusion weights. The MSA-enhanced block is inserted into a compact VGG-style backbone trained on the FER+ dataset using weighted sampling to counteract class imbalance. The resulting soft-max probabilities are linearly blended with prototype hues derived from a simplified Plutchik wheel to drive RGB lighting in real time. Results: The proposed VGGFac-STN-MSA model achieves 82.54% test accuracy on FER+, outperforming a CNN baseline and the reproduced Deep-Emotion architecture. Ablation shows that MSA contributes +1% accuracy. Continuous color blending yields smooth, intensity-aware lighting transitions in a proof-of-concept demo. Discussion: Our attention scheme is architecture-agnostic, adds minimal computational overhead, and markedly boosts FER accuracy on low-resolution faces. Coupling the probability distribution directly to the RGB gamut provides a fine-grained, perceptually meaningful channel for affect-adaptive AAL systems.