  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.
