Paper Info
Title
FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos
Abstract
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. We analyze the important ingredients of constructing such a novel dataset in three aspects: (1) multi-scene hierarchy and expression class, (2) generation of candidate video clips, (3) trusted manual labelling process. Based on these guidelines, we select 4 scenarios subdivided into 22 scenes, annotate 86k samples automatically obtained from 4k videos based on the well-designed workflow, and finally build 38,935 video clips labeled with 7 classic expressions. Experiment benchmarks on four kinds of baseline frame-works were also provided and further analysis on their performance across different scenes and some challenges for future research were given. Besides, we systematically investigate key components of DFER by ablation studies. The baseline framework and our project are available on https://github.com/wangyanckxx/FERV39k.
Year
DOI
Venue
2022
10.1109/CVPR52688.2022.02025
IEEE Conference on Computer Vision and Pattern Recognition
Keywords
DocType
Volume
Datasets and evaluation, Face and gestures
Conference
2022
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Yan Wang100.34
Yixuan Sun200.68
Yiwen Huang300.68
Zhongying Liu400.34
Shuyong Gao501.35
Wei Zhang626028.92
Weifeng Ge700.68
Wenqiang Zhang856.50