2022/03/25 by Andrey V. Savchenko, Savchenko, Andrey V.
Computer Science · Psychology · #68T10 #Computer Vision and Pattern Recognition (cs.CV) #Emotion and Mood Recognition #FOS: Computer and information sciences #I.4.9 #acm:68T10 #cs.CV #msc:68T10
paper · pdf · doi:10.48550/arxiv.2203.13436
accepted at CVPR Workshop ABAW3, 8 pages, 2 figures, 6 tables
openalex publication_date 2022/03/25 · openalex created_date 2022/04/03 · arxiv created 2022/05/24 · arxiv updated 2022/05/25 · openalex updated_date 2026/07/28
In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel frame-level emotion recognition algorithm by extracting facial features with the single EfficientNet model pre-trained on AffectNet. As a result, our approach may be implemented even for video analytics on mobile devices. Experimental results for the large scale Aff-Wild2 database from the third Affective Behavior Analysis in-the-wild (ABAW) Competition demonstrate that our simple model is significantly better when compared to the VggFace baseline. In particular, our method is characterized by 0.15-0.2 higher performance measures for validation sets in uni-task Expression Classification, Valence-Arousal Estimation and Expression Classification. Due to simplicity, our approach may be considered as a new baseline for all four sub-challenges.