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학술지 Toward Unbiased Facial Expression Recognition in the Wild via Cross-Dataset Adaptation
Cited 11 time in scopus Download 130 time Share share facebook twitter linkedin kakaostory
한병옥, 윤우한, 유장희, 김원화
IEEE Access, v.8, pp.159172-159181
20HS2400, 영유아/아동의 발달장애 조기선별을 위한 행동·반응 심리인지 AI 기술 개발, 유장희
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 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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