  Deep neural networks (DNNs) trained on large-scale datasets have recently
achieved impressive improvements in face recognition. But a persistent
challenge remains to develop methods capable of handling large pose variations
that are relatively underrepresented in training data. This paper presents a
method for learning a feature representation that is invariant to pose, without
requiring extensive pose coverage in training data. We first propose to
generate non-frontal views from a single frontal face, in order to increase the
diversity of training data while preserving accurate facial details that are
critical for identity discrimination. Our next contribution is to seek a rich
embedding that encodes identity features, as well as non-identity ones such as
pose and landmark locations. Finally, we propose a new feature reconstruction
metric learning to explicitly disentangle identity and pose, by demanding
alignment between the feature reconstructions through various combinations of
identity and pose features, which is obtained from two images of the same
subject. Experiments on both controlled and in-the-wild face datasets, such as
MultiPIE, 300WLP and the profile view database CFP, show that our method
consistently outperforms the state-of-the-art, especially on images with large
head pose variations. Detail results and resource are referred to
https://sites.google.com/site/xipengcshomepage/iccv2017
