Masked Face Recognition via Self-Attention Based Local Consistency Regularization

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Masked Face Recognition via Self-Attention Based Local Consistency Regularization
Title:
Masked Face Recognition via Self-Attention Based Local Consistency Regularization
Journal Title:
2022 IEEE International Conference on Image Processing (ICIP)
Publication URL:
Keywords:
Publication Date:
18 October 2022
Citation:
D. Lin, Y. Cheng, Y. Li, S. Prasad and A. Guo, Masked Face Recognition via Self-Attention Based Local Consistency Regularization. 2022 IEEE International Conference on Image Processing (ICIP), 2022, pp. 436-440, doi: 10.1109/ICIP46576.2022.9898076.
Abstract:
With the COVID-19 pandemic, one critical measure against infection is wearing masks. This measure poses a huge challenge to the existing face recognition systems by introducing heavy occlusions. In this paper, we propose an effective masked face recognition system. To alleviate the challenge of mask occlusion, we first exploit RetinaFace to achieve robust masked face detection and alignment. Secondly, we propose a deep CNN network for masked face recognition trained by minimizing ArcFace loss together with a local consistency regularization (LCR) loss. This facilitates the network to simultaneously learn globally discriminative face representations of different identities together with locally consistent representations between the non-occluded faces and their counterparts wearing synthesized facial masks. The experiments on the masked LFW dataset demonstrate that the proposed system can produce superior masked face recognition performance over multiple state-of-the-art methods. The proposed method is implemented in a portable Jetson Nano device which can achieve real-time masked face recognition.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
ISBN:
978-1-6654-9621-6
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