SaFace: Towards Scenario-aware Face Recognition via Edge Computing System


Zhe Zhou, Bingzhe Wu, Zheng Liang, Guangyu Sun, Chenren Xu, and Guojie Luo, Peking University


Deep Convolutional Neural Networks (CNNs) have achieved remarkable progress in the field of face recognition (FR). However, developing a robust FR system in the real-world is still challenging due to vast variance of illumination, visual quality, and camera angles in different scenarios. These factors may result in significant accuracy drop, if the pretrained model doesn’t have perfect generalization ability. To mitigate this issue, we present a solution named SAFACE, which helps to improve FR accuracy through unsupervised online-learning in an edge computing system. Specifically, we propose a novel scenario-aware FR flow, then decouple the flow into different phases and map each of them to different levels of a three-layer edge computing system. For evaluation, we implement a prototype and demonstrate its advantages in both improving recognition accuracy and reducing processing latency.

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@inproceedings {253360,
author = {Zhe Zhou and Bingzhe Wu and Zheng Liang and Guangyu Sun and Chenren Xu and Guojie Luo},
title = {{SaFace}: Towards Scenario-aware Face Recognition via Edge Computing System},
booktitle = {3rd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 20)},
year = {2020},
url = {},
publisher = {USENIX Association},
month = jun

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