Masked Face Detection Using A Two-stage Classification Approach In the COVID-19 Era
Bingshu Wang, Licheng Liu, C. L. Philip Chen · 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2021
Masked face detection is a challenging task in the surveillance applications due to complex backgrounds. In this paper, we propose a two-stage method for masked face detection: pre-detection and verification. Firstly, a masked face detector based on AdaBoost algorithm and histogram of orientation feature is exploited. It may provide sufficient candidate face regions. Secondly, a two-class classifier is trained by broad learning system, which is an incremental learning algorithm with high efficiency in training. It is used to distinguish realistic masked faces from background. Moreover, this paper proposes a masked face dataset that includes multiple masked faces captured from real-life scenes . It can be used for classifier training and evaluation. Experiments conducted on the dataset indicate the effectiveness of the proposed method with Recall 94.69% and Precision 97.72%.