Facial Expression Recognition Datasets

Facial expression recognition (FER) datasets are crucial for developing accurate and robust emotion recognition systems, aiming to bridge the gap between human and machine understanding of emotional expression. Current research focuses on addressing challenges like domain bias, low-light conditions, and the limitations of under-display cameras, employing techniques such as diffusion models, vision transformers, and vision-language models to improve generalization and accuracy. These advancements are significant for applications in various fields, including human-computer interaction, mental health assessment, and security systems, by enabling more reliable and context-aware emotion detection. The development of robust and diverse datasets, coupled with innovative model architectures, is driving progress towards more effective and ethically sound FER systems.