Facial Expression Analysis

Facial expression analysis aims to automatically recognize and interpret human emotions from facial images or videos, with applications ranging from human-computer interaction to mental health assessment. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs) to address challenges like head pose variation, illumination changes, and individual differences in expression. A key focus is improving robustness and generalizability across diverse datasets and contexts, often involving techniques like identity normalization and multimodal fusion. These advancements are driving progress in various fields, including healthcare, education, and the development of more emotionally intelligent systems.