AirFace: Lightweight and Efficient Model for Face Recognition

Xianyang Li, Feng Wang, Qinghao Hu, Cong Leng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 0-0

Abstract


With the development of convolutional neural network, significant progress has been made in computer vision tasks. However, the commonly used loss function softmax loss and highly efficient network architectures for common visual tasks are not as effective for face recognition. In this paper, we propose a novel loss function named Li-ArcFace based on ArcFace. Li-ArcFace takes the value of the angle through a linear function as the target logit rather than through cosine function, which has better convergence and performance on low dimensional embedding feature learning for face recognition. In terms of network architecture, we improved the the perfomance of MobileFaceNet by increasing the network depth, width and adding attention module. Besides, we found some useful training tricks for face recognition. Under all the above effects, we won the second place in the deepglint-light challenge of LFR2019.

Related Material


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[bibtex]
@InProceedings{Li_2019_ICCV,
author = {Li, Xianyang and Wang, Feng and Hu, Qinghao and Leng, Cong},
title = {AirFace: Lightweight and Efficient Model for Face Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}
}