Recognition system for masked face based on deep learning

Yinghui Kong; Shuaitong Zhang; Xinye Li; Ke Zhang; YinCheng Qi; Zhenbing Zhao

doi:10.1117/12.2604714

5 October 2021 Recognition system for masked face based on deep learning

Yinghui Kong, Shuaitong Zhang, Xinye Li, Ke Zhang, YinCheng Qi, Zhenbing Zhao

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 1191111 (2021) https://doi.org/10.1117/12.2604714
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

With the spread of the epidemic in the world, wearing masks has become the most simple and effective way to block the COVID-19. For the lack of data and model design to fit the epidemic scene, we propose an integrated masked face recognition system with three cascaded convolutional neural networks. Firstly, a SSD model is used to detect masked face to eliminate the interference of irrelevant background. Then, we use an Hourglass network to regress the key points of the occluded face and crop the aligned eye-brow area without mask. Finally, we finetune a pretrained FaceNet to fully adapt to the data of eye-brow regions. Experiments on numbers of laboratory and wild images proved that our method can recognize the subjects with mask effectively.

Citation Download Citation

Yinghui Kong, Shuaitong Zhang, Xinye Li, Ke Zhang, YinCheng Qi, and Zhenbing Zhao "Recognition system for masked face based on deep learning", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 1191111 (5 October 2021); https://doi.org/10.1117/12.2604714

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