Deep class-skewed learning for face recognition

Pingyu Wang

doi:10.1016/j.neucom.2019.04.085

Deep class-skewed learning for face recognition

2019-10-01

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DOI：10.1016/j.neucom.2019.04.085

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Face datasets often exhibit highly-skewed class distribution, i.e., rich classes contain a plenty amount of instances, while only few images belong to poor classes. To mitigate this issue, we explore deep class-skewed learning from two aspects in this paper: feature augmentation and feature normalization. To deal with the imbalance distribution problem, we put forward a novel feature augmentation method termed Large Margin Feature Augmentation (LMFA) to augment hard features and equalize class distribution, leading to balanced classification boundaries between rich and poor classes. By considering the distribution gap between training and testing features, A novel feature normalization called Transferable Domain Normalization (TDN) is proposed to normalize domain-specific features to obey an identical Gaussian distribution, and enhance the feature generalization. Extensive experiments are conducted on five popular face recognition datasets including LFW, YTF, CFP, AgeDB and MegaFace. We achieve remarkable results on par with or better than the state-of-the-art methods, which demonstrate the effectiveness of our proposed learning class-balanced features. (C) 2019 Published by Elsevier B.V.

Keyword:

Face recognition

Imbalanced learning

Feature augmentation

Feature normalization

Deep class-skewed learning for face recognition

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