  The past few years have witnessed great progress in the domain of face
recognition thanks to advances in deep learning. However, cross pose face
recognition remains a significant challenge. It is difficult for many deep
learning algorithms to narrow the performance gap caused by pose variations;
the main reasons for this relate to the intra-class discrepancy between face
images in different poses and the pose imbalances of training datasets.
Learning pose-robust features by traversing to the feature space of frontal
faces provides an effective and cheap way to alleviate this problem. In this
paper, we present a method for progressively transforming profile face
representations to the canonical pose with an attentive pair-wise loss.
Firstly, to reduce the difficulty of directly transforming the profile face
features into a frontal pose, we propose to learn the feature residual between
the source pose and its nearby pose in a block-byblock fashion, and thus
traversing to the feature space of a smaller pose by adding the learned
residual. Secondly, we propose an attentive pair-wise loss to guide the feature
transformation progressing in the most effective direction. Finally, our
proposed progressive module and attentive pair-wise loss are light-weight and
easy to implement, adding only about 7:5% extra parameters. Evaluations on the
CFP and CPLFW datasets demonstrate the superiority of our proposed method. Code
is available at https://github.com/hjy1312/AGPM.
