Compound Expression Recognition via Multi Model Ensemble for the ABAW7 Challenge

• URL: http://arxiv.org/abs/2407.12257v2
• Date: Fri, 26 Jul 2024 08:46:26 GMT
• Title: Compound Expression Recognition via Multi Model Ensemble for the ABAW7 Challenge
• Authors: Xuxiong Liu, Kang Shen, Jun Yao, Boyan Wang, Minrui Liu, Liuwei An, Zishun Cui, Weijie Feng, Xiao Sun,
• Abstract summary: Compound Expression Recognition (CER) is vital for effective interpersonal interactions. In this paper, we propose an ensemble learning-based solution to address this complexity. 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.
• Score: 6.26485278174662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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