Advancements and Challenges in Emotion Recognition from Facial Expressions: A Comprehensive Review

Abstract

Emotion recognition from facial expressions (FER) is a crucial component of human-computer interaction, enabling machines to comprehend and respond to human emotions. This review examines advancements in FER technologies, highlighting key models such as Deep Residual Networks (ResNet), BReG-NeXt-50, and VGGNet, as well as innovative data augmentation techniques that optimize model performance. Despite significant progress, challenges remain, including class imbalance, generalization issues, and real-time processing constraints. Future research directions are proposed, focusing on the development of hybrid models, dynamic data augmentation, and cross-domain adaptation strategies, as well as ethical considerations and the importance of interdisciplinary collaboration. By addressing these challenges and exploring innovative methodologies, this review aims to advance the development of more robust and effective emotion recognition systems, ultimately facilitating better human-computer interactions across various applications.