Expression Recognition

Facial expression recognition (FER) aims to automatically identify human emotions from facial images or videos, seeking to improve accuracy and interpretability. Current research emphasizes developing robust models, including convolutional neural networks (CNNs), vision transformers (ViTs), and generative adversarial networks (GANs), often incorporating techniques like multi-task learning, self-supervised learning, and attention mechanisms to handle challenges such as pose variation, data imbalance, and noisy labels. This field is significant for its potential applications in various domains, including healthcare (e.g., depression detection), human-computer interaction, and security, driving efforts to create more accurate, efficient, and unbiased FER systems. Furthermore, there's a growing focus on improving the interpretability of these systems and mitigating biases related to demographics.