Facial Expression Recognition with Deep Learning and Attention Mechanisms: A Systematic Review

This comprehensive literature review meticulously examines the dynamic field of Facial Expression Recognition (FER), particularly highlighting the transformative role of deep learning and attention mechanisms in advancing the state of the art. By meticulously analyzing 53 seminal papers, we highlight the dominant roles of CNN, ResNet, and LSTM, with particular attention to the Region Attention Network (RAN). These technologies have significantly advanced the accuracy and efficiency of FER systems, marking a shift towards more sophisticated analysis methods. Our review not only synthesizes the current state of FER advancements but also identifies key trends, challenges, and ethical considerations shaping future research directions. We emphasize the need for culturally sensitive, robust FER systems and the potential of multimodal approaches to enrich the understanding and interpretation of facial expressions. This review aims to provide a foundational resource for future studies, guiding the development of more nuanced, effective, and ethical FER technologies, with a special note on its emerging applications in monitoring and enhancing employee emotional well-being in Human Resources (HR).