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

2023/03/16 by Andrey V. Savchenko, Savchenko, Andrey V.
Computer Science · Neuroscience · Psychology · #68T10 #Computer Vision and Pattern Recognition (cs.CV) #EEG and Brain-Computer Interfaces #Emotion and Mood Recognition #FOS: Computer and information sciences #Human Pose and Action Recognition #I.4.9

paper · pdf · doi:10.48550/arxiv.2303.09162

openalex publication_date 2023/03/16 · openalex created_date 2023/03/19 · openalex updated_date 2026/07/28

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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