Inter-class angular margin loss for face recognition

作者：

Highlights：

• We propose an IAM loss to increase the inter-class margin adaptively.

• As a regularization term, IAM loss can be applied to current state-of-the-art methods and increase the recognition accuracy further.

• Experiments on LFW, YTF, CFP and MegaFace can verify the effectiveness of our IAM loss.

摘要

•We propose an IAM loss to increase the inter-class margin adaptively.•As a regularization term, IAM loss can be applied to current state-of-the-art methods and increase the recognition accuracy further.•Experiments on LFW, YTF, CFP and MegaFace can verify the effectiveness of our IAM loss.

论文关键词：Face recognition,IAM loss,Inter-class variance,Intra-class distance,Softmax loss

论文评审过程：Received 17 March 2019, Revised 23 July 2019, Accepted 10 September 2019, Available online 13 September 2019, Version of Record 5 October 2019.

论文官网地址：https://doi.org/10.1016/j.image.2019.115636