AUTHOR=Russo Samuele , Tibermacine Imad Eddine , Randieri Cristian , Rabehi Abdelaziz , Alharbi Amal H. , El-kenawy El-Sayed M. , Napoli Christian 

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

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 19 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1622194

DOI=10.3389/fnins.2025.1622194

ISSN=1662-453X

ABSTRACT=IntroductionFacial 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.MethodsWe 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.ResultsThe 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.DiscussionOur 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.