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Journal Article
Toward Unbiased Facial Expression Recognition in the Wild via Cross-Dataset Adaptation
- Authors
- Byungok Han, Woo-Han Yun, Jang-Hee Yoo, Won Hwa Kim
- Issue Date
- 2020-08
- Citation
- IEEE Access, v.8, pp.159172-159181
- ISSN
- 2169-3536
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- 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.
- KSP Keywords
- Computer Vision(CV), Dataset bias, Facial expression recognition(FER), Facial image, Global agreement, Improved performance, In-the-wild, Label distributions, Large-scale study, Learning approach, Machine Learning Algorithms
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
