FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos

Understand

Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos.

• It is still ambiguous to evaluate whether performances of existing methods remain satisfactory in real-world application-oriented scenes.
• For example, the "Happy" expression with high intensity in Talk-Show is more discriminating than the same expression with low intensity in Official-Event.
• To fill this gap, we build a large-scale multi-scene dataset, coined as FERV39k.