Toward Unbiased Facial Expression Recognition in the Wild via Cross-Dataset Adaptation
SCIE
SCOPUS
- Title
- Toward Unbiased Facial Expression Recognition in the Wild via Cross-Dataset Adaptation
- Authors
- Byungok Han; Woo-Han Yun; Jang-Hee Yoo; KIM, WON HWA
- Date Issued
- 2020-08
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Abstract
- Despite various success in computer vision with facial images (e.g., face detection, recognition, and generation), facial expression recognition is still a challenging problem yet to be solved. This is because of simple but fundamental bottlenecks: 1) no global agreement on different facial expressions, 2) significant dataset biases that prevent cross-dataset analysis for a large-scale study, and 3) high class imbalance in in-the-wild datasets that causes inconsistency in predicting expressions in images using a machine learning algorithm. To tackle these issues, we propose a novel Deep Learning approach via adaptive cross-dataset scheme. We combine multiple in-the-wild datasets to secure sufficient training samples while minimizing dataset bias using ideas of reversal gradients to retain generality. For this, we introduce a flexible objective function that can control for skewed label distributions in the dataset. Incorporating these ideas, together with the ResNet pipeline as a backbone, we carried extensive experiments to validate our ideas using three independent in-the-wild facial expression datasets, which first confirmed bias from different datasets and yielded improved performance on facial expression recognition using the multi-site dataset.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/104910
- DOI
- 10.1109/ACCESS.2020.3018738
- ISSN
- 2169-3536
- Article Type
- Article
- Citation
- IEEE Access, vol. 8, page. 159172 - 159181, 2020-08
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- There are no files associated with this item.
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