  In this paper, we evaluate the effects of occlusions in the performance of a
face recognition pipeline that uses a ResNet backbone. The classifier was
trained on a subset of the CelebA-HQ dataset containing 5,478 images from 307
classes, to achieve top-1 error rate of 17.91%. We designed 8 different
occlusion masks which were applied to the input images. This caused a
significant drop in the classifier performance: its error rate for each mask
became at least two times worse than before. In order to increase robustness
under occlusions, we followed two approaches. The first is image inpainting
using the pre-trained pluralistic image completion network. The second is
Cutmix, a regularization strategy consisting of mixing training images and
their labels using rectangular patches, making the classifier more robust
against input corruptions. Both strategies revealed effective and interesting
results were observed. In particular, the Cutmix approach makes the network
more robust without requiring additional steps at the application time, though
its training time is considerably longer. Our datasets containing the different
occlusion masks as well as their inpainted counterparts are made publicly
available to promote research on the field.

Computer Vision and Pattern Recognition;Artificial Intelligence