EmotiEffNet Facial Features in Uni-task Emotion Recognition in Video at ABAW-5 competition

• URL: http://arxiv.org/abs/2303.09162v1
• Date: Thu, 16 Mar 2023 08:57:33 GMT
• Title: EmotiEffNet Facial Features in Uni-task Emotion Recognition in Video at ABAW-5 competition
• Authors: Andrey V. Savchenko
• Abstract summary: We present the results of our team for the fifth Affective Behavior Analysis in-the-wild (ABAW) competition. The usage of the pre-trained convolutional networks from the EmotiEffNet family for frame-level feature extraction is studied.
• Score: 7.056222499095849
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this article, the results of our team for the fifth Affective Behavior Analysis in-the-wild (ABAW) competition are presented. The usage of the pre-trained convolutional networks from the EmotiEffNet family for frame-level feature extraction is studied. In particular, we propose an ensemble of a multi-layered perceptron and the LightAutoML-based classifier. The post-processing by smoothing the results for sequential frames is implemented. Experimental results for the large-scale Aff-Wild2 database demonstrate that our model achieves a much greater macro-averaged F1-score for facial expression recognition and action unit detection and concordance correlation coefficients for valence/arousal estimation when compared to baseline.

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