Facial Expression Classification Using Fusion of Deep Neural Network in Video
Abstract
For computers to recognize human emotions, expression classification is an equally important problem in the human-computer interaction area. In the 3rd Affective Behavior Analysis In-The-Wild competition, the task of expression classification includes eight classes with six basic expressions of human faces from videos. In this paper, we employ a transformer mechanism to encode the robust representation from the backbone. Fusion of the robust representations plays an important role in the expression classification task. Our approach achieves 30.35% and 28.60% for the F1 score on the validation set and the test set, respectively. This result shows the effectiveness of the proposed architecture based on the Aff-Wild2 dataset and our team archives 5th for the expression classification task in the 3rd Affective Behavior Analysis In-The-Wild competition.
Cite
Text
Phan et al. "Facial Expression Classification Using Fusion of Deep Neural Network in Video." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022. doi:10.1109/CVPRW56347.2022.00280Markdown
[Phan et al. "Facial Expression Classification Using Fusion of Deep Neural Network in Video." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022.](https://mlanthology.org/cvprw/2022/phan2022cvprw-facial/) doi:10.1109/CVPRW56347.2022.00280BibTeX
@inproceedings{phan2022cvprw-facial,
title = {{Facial Expression Classification Using Fusion of Deep Neural Network in Video}},
author = {Phan, Kim Ngan and Nguyen, Hong Hai and Huynh, Van Thong and Kim, Soo-Hyung},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2022},
pages = {2506-2510},
doi = {10.1109/CVPRW56347.2022.00280},
url = {https://mlanthology.org/cvprw/2022/phan2022cvprw-facial/}
}