Multi Model Ensemble for Compound Expression Recognition

Jun Yu, Jichao Zhu, Wangyuan Zhu, Zhongpeng Cai, Gongpeng Zhao, Zhihong Wei, Guochen Xie, Zerui Zhang, Qingsong Liu, Jiaen Liang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4873-4879

Abstract

Compound Expression Recognition (CER) plays a crucial role in interpersonal interactions. Due to the complexity of human emotional expressions which leads to the existence of compound expressions it is necessary to consider both local and global facial expressions comprehensively for recognition. In this paper to address this issue we propose a solution for compound expression recognition based on ensemble learning methods. Specifically our task is classification. We trained three expression classification models based on convolutional networks (ResNet50) Vision Transformers and multi-scale local attention networks respectively. Then by using late fusion integrated the outputs of three models to predict the final result leveraging the strengths of different models. Our method achieves high accuracy on RAF-DB and in sixth Affective Behavior Analysis in-the-wild (ABAW) Challenge achieves an F1 score of 0.224 on the test set of C-EXPR-DB.

Related Material

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[bibtex]
@InProceedings{Yu_2024_CVPR, author = {Yu, Jun and Zhu, Jichao and Zhu, Wangyuan and Cai, Zhongpeng and Zhao, Gongpeng and Wei, Zhihong and Xie, Guochen and Zhang, Zerui and Liu, Qingsong and Liang, Jiaen}, title = {Multi Model Ensemble for Compound Expression Recognition}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {4873-4879} }