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Inter-class angular margin loss for face recognition

Yang, W; Sun, J; Gao, R; Xue, J-H; Liao, Q; (2019) Inter-class angular margin loss for face recognition. Signal Processing: Image Communication , Article 115636. 10.1016/j.image.2019.115636. (In press).

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Abstract

Increasing inter-class variance and shrinking intra-class distance are two main concerns and efforts in face recognition. In this paper, we propose a new loss function termed inter-class angular margin (IAM) loss aiming to enlarge the inter-class variance. Instead of restricting the inter-class margin to be a constant in existing methods, our IAM loss adaptively penalizes smaller inter-class angles more heavily and successfully makes the angular margin between classes larger, which can significantly enhance the discrimination of facial features. The IAM loss can be readily introduced as a regularization term for the widely-used Softmax loss and its recent variants to further improve their performances. We also analyze and verify the appropriate range of the regularization hyper-parameter from the perspective of backpropagation. For illustrative purposes, our model is trained on CASIA-WebFace and tested on the LFW, CFP, YTF and MegaFace datasets; the experimental results show that the IAM loss is quite effective to improve state-of-the-art algorithms.

Type: Article
Title: Inter-class angular margin loss for face recognition
DOI: 10.1016/j.image.2019.115636
Publisher version: https://doi.org/10.1016/j.image.2019.115636
Language: English
Additional information: Face recognition, IAM loss, Inter-class variance, Intra-class distance, Softmax loss
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10081657
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