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Current benchmarks for facial expression recognition (FER) mainly focus onstatic images, while there are limited datasets for FER in videos. It is stillambiguous to evaluate whether performances of existing methods remainsatisfactory in real-world application-oriented scenes. For example, the"Happy" expression with high intensity in Talk-Show is more discriminating thanthe same expression with low intensity in Official-Event. To fill this gap, webuild a large-scale multi-scene dataset, coined as FERV39k. We analyze theimportant ingredients of constructing such a novel dataset in three aspects:(1) multi-scene hierarchy and expression class, (2) generation of candidatevideo clips, (3) trusted manual labelling process. Based on these guidelines,we select 4 scenarios subdivided into 22 scenes, annotate 86k samplesautomatically obtained from 4k videos based on the well-designed workflow, andfinally build 38,935 video clips labeled with 7 classic expressions. Experimentbenchmarks on four kinds of baseline frameworks were also provided and furtheranalysis on their performance across different scenes and some challenges forfuture research were given. Besides, we systematically investigate keycomponents of DFER by ablation studies. The baseline framework and our projectwill be available.

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