Masked facial region recognition using human pose estimation and broad learning system

Hongli Xiao; Bingshu Wang; Jiangbin Zheng; Jin Fang; Zhulin Liu; C. L. Philip Chen

doi:10.1117/12.2643023

12 October 2022 Masked facial region recognition using human pose estimation and broad learning system

Hongli Xiao, Bingshu Wang, Jiangbin Zheng, Jin Fang, Zhulin Liu, C. L. Philip Chen

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123420S (2022) https://doi.org/10.1117/12.2643023
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

COVID-19 and its variants have been posing a large risk to people around the world since the outbreak of the disease. Many techniques like AI are explored to help combat epidemics. People are required or forced to wear a mask to fight against COVID-19 epidemics worldwide. It brings new challenges to the task of masked facial region recognition. When facial regions are occluded by masks, it will result in some failures of face detection algorithms. In this paper, we propose a method to recognize masked faces. It mainly includes three parts. Firstly, the human pose is estimated to produce a series of key points. It is implemented by OpenPose. Secondly, a key-points location strategy is designed to capture the masked facial regions. It can locate the positions of faces accurately. Thirdly, the broad learning system, which is also an incremental learning algorithm, is employed to recognize the classes of candidate regions. Experiments conducted on some datasets shed light on the effectiveness of the proposed method.

Citation Download Citation

Hongli Xiao, Bingshu Wang, Jiangbin Zheng, Jin Fang, Zhulin Liu, and C. L. Philip Chen "Masked facial region recognition using human pose estimation and broad learning system", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123420S (12 October 2022); https://doi.org/10.1117/12.2643023

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