  Since the beginning of world-wide COVID-19 pandemic, facial masks have been
recommended to limit the spread of the disease. However, these masks hide
certain facial attributes. Hence, it has become difficult for existing face
recognition systems to perform identity verification on masked faces. In this
context, it is necessary to develop masked Face Recognition (MFR) for
contactless biometric recognition systems. Thus, in this paper, we propose
Complementary Attention Learning and Multi-Focal Spatial Attention that
precisely removes masked region by training complementary spatial attention to
focus on two distinct regions: masked regions and backgrounds. In our method,
standard spatial attention and networks focus on unmasked regions, and extract
mask-invariant features while minimizing the loss of the conventional Face
Recognition (FR) performance. For conventional FR, we evaluate the performance
on the IJB-C, Age-DB, CALFW, and CPLFW datasets. We evaluate the MFR
performance on the ICCV2021-MFR/Insightface track, and demonstrate the improved
performance on the both MFR and FR datasets. Additionally, we empirically
verify that spatial attention of proposed method is more precisely activated in
unmasked regions.
