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Double Additive Margin Softmax Loss for Face Recognition

College of Information Engineering, Yangzhou University, Yangzhou 225127, China
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(1), 60;
Received: 30 October 2019 / Revised: 14 December 2019 / Accepted: 17 December 2019 / Published: 19 December 2019
(This article belongs to the Special Issue Advanced Biometrics with Deep Learning)
Learning large-margin face features whose intra-class variance is small and inter-class diversity is one of important challenges in feature learning applying Deep Convolutional Neural Networks (DCNNs) for face recognition. Recently, an appealing line of research is to incorporate an angular margin in the original softmax loss functions for obtaining discriminative deep features during the training of DCNNs. In this paper we propose a novel loss function, termed as double additive margin Softmax loss (DAM-Softmax). The presented loss has a clearer geometrical explanation and can obtain highly discriminative features for face recognition. Extensive experimental evaluation of several recent state-of-the-art softmax loss functions are conducted on the relevant face recognition benchmarks, CASIA-Webface, LFW, CALFW, CPLFW, and CFP-FP. We show that the proposed loss function consistently outperforms the state-of-the-art. View Full-Text
Keywords: Softmax; angular margin; ResNet; face recognition Softmax; angular margin; ResNet; face recognition
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MDPI and ACS Style

Zhou, S.; Chen, C.; Han, G.; Hou, X. Double Additive Margin Softmax Loss for Face Recognition. Appl. Sci. 2020, 10, 60.

AMA Style

Zhou S, Chen C, Han G, Hou X. Double Additive Margin Softmax Loss for Face Recognition. Applied Sciences. 2020; 10(1):60.

Chicago/Turabian Style

Zhou, Shengwei, Caikou Chen, Guojiang Han, and Xielian Hou. 2020. "Double Additive Margin Softmax Loss for Face Recognition" Applied Sciences 10, no. 1: 60.

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