Abstract Unconstrained face recognition still remains a challenging task due to various factors such as pose, expression, illumination, partial occlusion, etc. In particular, the most significant appearance variations are stemmed from poses which leads to severe performance degeneration. In this paper, we propose a novel Deformable Face Net (DFN) to handle the pose variations for face recognition. The deformable convolution module attempts to simultaneously learn face recognition oriented alignment and identity-preserving feature extraction. The displacement consistency loss (DCL) is proposed as a regularization term to enforce the learnt displacement fields for aligning faces to be locally consistent both in the orientation and amplitude since faces possess strong structure. Moreover, the identity consistency loss (ICL) and the pose-triplet loss (PTL) are designed to minimize the intra-class feature variation caused by different poses and maximize the inter-class feature distance under the same poses. The proposed DFN can effectively handle pose invariant face recognition (PIFR). Extensive experiments show that the proposed DFN outperforms the state-of-the-art methods, especially on the datasets with large poses.

中文翻译：

---

用于姿态不变人脸识别的可变形人脸网

摘要 由于姿势、表情、光照、部分遮挡等各种因素，无约束人脸识别仍然是一项具有挑战性的任务。特别是，最显着的外观变化源于姿势，导致严重的性能退化。在本文中，我们提出了一种新颖的可变形人脸网络（DFN）来处理人脸识别的姿势变化。可变形卷积模块尝试同时学习面向人脸识别的对齐和身份保留特征提取。位移一致性损失 (DCL) 被提议作为正则化项，以强制学习的位移场对齐人脸在方向和幅度上局部一致，因为人脸具有很强的结构。而且，身份一致性损失（ICL）和姿势三元组损失（PTL）旨在最小化不同姿势引起的类内特征变化，并最大化相同姿势下的类间特征距离。所提出的 DFN 可以有效地处理姿势不变人脸识别 (PIFR)。大量实验表明，所提出的 DFN 优于最先进的方法，尤其是在具有大姿势的数据集上。