Cropping and attention based approach for masked face recognition
The global epidemic of COVID 19 makes people realize that wearing a mask is one of the most effective ways to protect ourselves from virus infections, which poses serious challenges for the existing face recognition system. To tackle the difficulties, a new method for masked face recognition is proposed by integrating a cropping based approach with the Convolutional Block Attention Module (CBAM) The optimal cropping is explored for each case, while the CBAM module is adopted to focus on the regions around eyes. Two special application scenarios, using faces without mask for training to recognize masked faces, and using masked faces for training to recognize faces without mask, have also been studied. Comprehensive experiments on SMFRD, CISIA Webface, AR and Extend Yela B datasets show that the proposed approach can significantly improve the performance of masked face recognition compared with other state of the art approaches.
Appl. Intell.