Sample-Level and Class-Level Adaptive Training for Face Recognition

Marginal softmax loss function has been widely used for face recognition, where a universal angular margin is added between weight prototypes. However, this method neglects the differences between classes and samples. On class-level, the imbalanced real world training dataset requires different marg...

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Bibliographic Details
Main Authors: Wang, Mengjiao, Liu, Rujie, Abe, Narishige, Tomoaki, Matsunami, Hidetsugu, Uchida, Lina, Septiana
Format: Conference Proceeding
Language:English
Subjects:
Adaptive Training
Boosting
Face recognition
Head
Marginal Softmax
Prototypes
Tail
Training
Upper bound
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Summary:Marginal softmax loss function has been widely used for face recognition, where a universal angular margin is added between weight prototypes. However, this method neglects the differences between classes and samples. On class-level, the imbalanced real world training dataset requires different margin for the head and tail classes to equally squeeze each class's feature space. On the sample-level, it's also necessary to assign larger importance for the hard samples during training. In this paper, we address these two issues by combining two strategies: (1) explicitly assign the adaptive margin according to the image quantity so that the margin is enlarged for the tail classes; (2) semantically identify the 'hard positive, samples and misclassified samples [1] to attach adaptive weights to increase the training emphasis on these samples. Extensive experiments on LFW/CFP/AGEDB and IJB-B/IJB-C show our method's effectiveness.
ISSN:1945-788X
DOI:10.1109/ICME52920.2022.9859920