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학술지 Toward Unbiased Facial Expression Recognition in the Wild via Cross-Dataset Adaptation
Cited 6 time in scopus Download 19 time Share share facebook twitter linkedin kakaostory
저자
한병옥, 윤우한, 유장희, 김원화
발행일
202008
출처
IEEE Access, v.8, pp.159172-159181
ISSN
2169-3536
출판사
IEEE
DOI
https://dx.doi.org/10.1109/ACCESS.2020.3018738
협약과제
20HS2400, 영유아/아동의 발달장애 조기선별을 위한 행동·반응 심리인지 AI 기술 개발, 유장희
초록
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.
키워드
Cross-dataset bias, Deep learning, Domain adaptation, Facial expression recognition, In-the-wild dataset
KSP 제안 키워드
Computer Vision(CV), Dataset bias, Face detection, Facial Expression Recognition(FER), Facial image, In-the-wild, Label distributions, Large-scale study, Learning approach, Machine Learning Algorithms, Multi-site
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