Compound Expression Recognition (CER) is vital for effective interpersonal interactions. Human emotional expressions are inherently complex due to the presence of compound expressions, requiring the consideration of both local and global facial cues for accurate judgment. In this paper, we propose an ensemble learning-based solution to address this complexity. Our approach involves training three distinct expression classification models using convolutional networks, Vision Transformers, and multiscale local attention networks. By employing late fusion for model ensemble, we combine the outputs of these models to predict the final results. Our method demonstrates high accuracy on the RAF-DB datasets and is capable of recognizing expressions in certain portions of the C-EXPR-DB through zero-shot learning.
@article{arxiv.2407.12257,
title = {Compound Expression Recognition via Multi Model Ensemble for the ABAW7 Challenge},
author = {Xuxiong Liu and Kang Shen and Jun Yao and Boyan Wang and Minrui Liu and Liuwei An and Zishun Cui and Weijie Feng and Xiao Sun},
journal= {arXiv preprint arXiv:2407.12257},
year = {2024}
}
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arXiv admin note: text overlap with arXiv:2403.12572 by other authors