Improving Representation Consistency With Pairwise Loss for Masked Face Recognition

Hanjie Qian, Panpan Zhang, Sijie Ji, Shuxin Cao, Yuecong Xu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 1462-1467

Abstract

Given the coronavirus disease (COVID-19) pandemic,people need to wear masks to protect themselves and reduce the spread of COVID, which bring new challenge to traditional face recognition task. Since features like the nose andmouth, which are well distinguishable, are hidden under themask, traditional methods are no longer simply applicable,even though they once achieved a high degree of accuracy.In response to this problem, the Masked Face RecognitionChallenge&Workshop (MFR) was held in conjunction withthe International Conference on Computer Vision (ICCV)2021. This article details a method that combining the classic ArcFace and pairwise loss to target the new masked facerecognition task. So far, our method has achieved the second place in the competition.

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[bibtex]
@InProceedings{Qian_2021_ICCV, author = {Qian, Hanjie and Zhang, Panpan and Ji, Sijie and Cao, Shuxin and Xu, Yuecong}, title = {Improving Representation Consistency With Pairwise Loss for Masked Face Recognition}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {1462-1467} }