Compound Expression Recognition via Multi Model Ensemble

• URL: http://arxiv.org/abs/2403.12572v1
• Date: Tue, 19 Mar 2024 09:30:56 GMT
• Title: Compound Expression Recognition via Multi Model Ensemble
• Authors: Jun Yu, Jichao Zhu, Wangyuan Zhu,
• Abstract summary: Compound Expression Recognition plays a crucial role in interpersonal interactions. We propose a solution based on ensemble learning methods for Compound Expression Recognition. Our method achieves high accuracy on RAF-DB and is able to recognize expressions through zero-shot on certain portions of C-EXPR-DB.
• Score: 8.529105068848828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compound Expression Recognition (CER) plays a crucial role in interpersonal interactions. Due to the existence of Compound Expressions , human emotional expressions are complex, requiring consideration of both local and global facial expressions to make judgments. In this paper, to address this issue, we propose a solution based on ensemble learning methods for Compound Expression Recognition. Specifically, our task is classification, where we train three expression classification models based on convolutional networks, Vision Transformers, and multi-scale local attention networks. Then, through model ensemble using late fusion, we merge the outputs of multiple models to predict the final result. Our method achieves high accuracy on RAF-DB and is able to recognize expressions through zero-shot on certain portions of C-EXPR-DB.

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