Attention-guided Progressive Mapping for Profile Face Recognition

• URL: http://arxiv.org/abs/2106.14124v2
• Date: Tue, 29 Jun 2021 10:33:31 GMT
• Title: Attention-guided Progressive Mapping for Profile Face Recognition
• Authors: Junyang Huang and Changxing Ding
• Abstract summary: Cross pose face recognition remains a significant challenge. Learning pose-robust features by traversing to the feature space of frontal faces provides an effective and cheap way to alleviate this problem.
• Score: 12.792576041526289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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