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.
@article{arxiv.2303.09162,
title = {EmotiEffNet Facial Features in Uni-task Emotion Recognition in Video at ABAW-5 competition},
author = {Andrey V. Savchenko},
journal= {arXiv preprint arXiv:2303.09162},
year = {2023}
}