TY - JOUR AU - Li, Pengyu AU - Wang, Biao AU - Zhang, Lei PY - 2021/05/18 Y2 - 2024/03/29 TI - Adversarial Pose Regression Network for Pose-Invariant Face Recognitions JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 3 SE - AAAI Technical Track on Computer Vision II DO - 10.1609/aaai.v35i3.16289 UR - https://ojs.aaai.org/index.php/AAAI/article/view/16289 SP - 1940-1948 AB - Face recognition has achieved significant progress in recent years. However, the large pose variation between face images remains a challenge in face recognition. We observe that the pose variation in the hidden feature maps is one of the most critical factors to hinder the representations from being pose-invariant. Based on the observation, we propose an Adversarial Pose Regression Network (APRN) to extract pose-invariant identity representations by disentangling their pose variation in hidden feature maps. To model the pose discriminator in APRN as a regression task in its 3D space, we also propose an Adversarial Regression Loss Function and extend the adversarial learning from classification problems to regression problems in this paper. Our APRN is a plug-and-play structure that can be embedded in other state-of-the-art face recognition algorithms to improve their performance additionally. The experiments show that the proposed APRN consistently and significantly boosts the performance of baseline networks without extra computational costs in the inference phase. APRN achieves comparable or even superior to the state-of-the-art on CFP, Multi-PIE, IJB-A and MegaFace datasets. The code will be released, hoping to nourish our proposals to other computer vision fields ER -