  Wearing a mask has proven to be one of the most effective ways to prevent the
transmission of SARS-CoV-2 coronavirus. However, wearing a mask poses
challenges for different face recognition tasks and raises concerns about the
performance of masked face presentation detection (PAD). The main issues facing
the mask face PAD are the wrongly classified bona fide masked faces and the
wrongly classified partial attacks (covered by real masks). This work addresses
these issues by proposing a method that considers partial attack labels to
supervise the PAD model training, as well as regional weighted inference to
further improve the PAD performance by varying the focus on different facial
areas. Our proposed method is not directly linked to specific network
architecture and thus can be directly incorporated into any common or
custom-designed network. In our work, two neural networks (DeepPixBis and
MixFaceNet) are selected as backbones. The experiments are demonstrated on the
collaborative real mask attack (CRMA) database. Our proposed method outperforms
established PAD methods in the CRMA database by reducing the mentioned
shortcomings when facing masked faces. Moreover, we present a detailed
step-wise ablation study pointing out the individual and joint benefits of the
proposed concepts on the overall PAD performance.
