Facial Expression

Facial expression research aims to automatically recognize and understand human emotions from facial movements, enabling applications in human-computer interaction, mental health assessment, and other fields. Current research focuses on improving the accuracy and robustness of emotion recognition models, particularly under challenging conditions like partial occlusion or limited data, often employing deep learning architectures such as Vision Transformers (ViTs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), along with techniques like data augmentation and multimodal fusion. These advancements are driving progress in areas like real-time emotion analysis, improved understanding of complex emotions, and the development of more accurate and fair facial analysis tools.