  With the recent advancement of deep convolutional neural networks,
significant progress has been made in general face recognition. However, the
state-of-the-art general face recognition models do not generalize well to
occluded face images, which are exactly the common cases in real-world
scenarios. The potential reasons are the absences of large-scale occluded face
data for training and specific designs for tackling corrupted features brought
by occlusions. This paper presents a novel face recognition method that is
robust to occlusions based on a single end-to-end deep neural network. Our
approach, named FROM (Face Recognition with Occlusion Masks), learns to
discover the corrupted features from the deep convolutional neural networks,
and clean them by the dynamically learned masks. In addition, we construct
massive occluded face images to train FROM effectively and efficiently. FROM is
simple yet powerful compared to the existing methods that either rely on
external detectors to discover the occlusions or employ shallow models which
are less discriminative. Experimental results on the LFW, Megaface challenge 1,
RMF2, AR dataset and other simulated occluded/masked datasets confirm that FROM
dramatically improves the accuracy under occlusions, and generalizes well on
general face recognition. Code is available at
https://github.com/haibo-qiu/FROM
