An efficient masked-face recognition technique to combat with COVID-19

ABSTRACT

Since the major COVID-19 pandemic hit the world, using face masks has become an important part of our daily lives. Not wearing masks for a moment when out¬side can be a life-threatening mistake. But the mask covers the lower portion of the face region. So we were not able to extract important features from the lower region. That is why in this paper, an efficient face recognition scheme has been presented by integrating discrete curvelet transform (DCT), compressive sensing (CS), and principal component analysis (PCA) to improve the face recognition rate of masked faces. The use of discrete curvelet transform (DCT) provides an enhancement of edge information of face images by applying an image fusion technique. To extract the feature vector in lower dimensional feature space, PCA has been applied on the fused images. Finally, the performance of this proposed technique is tested by using compressive sensing-based classifier. Extensive exper¬iments are simulated on masked and unmasked faces, and our method performs better than the conventional PCA. © 2023 Scrivener Publishing LLC. All rights reserved.