  Photos of faces captured in unconstrained environments, such as large crowds,
still constitute challenges for current face recognition approaches as often
faces are occluded by objects or people in the foreground. However, few studies
have addressed the task of recognizing partial faces. In this paper, we propose
a novel approach to partial face recognition capable of recognizing faces with
different occluded areas. We achieve this by combining attentional pooling of a
ResNet's intermediate feature maps with a separate aggregation module. We
further adapt common losses to partial faces in order to ensure that the
attention maps are diverse and handle occluded parts. Our thorough analysis
demonstrates that we outperform all baselines under multiple benchmark
protocols, including naturally and synthetically occluded partial faces. This
suggests that our method successfully focuses on the relevant parts of the
occluded face.
