Abstract
Pose-invariant face recognition has become a challenging problem for modern AI-based face recognition systems. It aims at matching a profile face captured in the wild with a frontal face registered in a database. Existing methods perform face frontalization via either generative models or learning a pose robust feature representation. In this paper, a new method is presented to perform face frontalization and recognition within the feature space. First, a novel feature space pose frontalization module (FSPFM) is proposed to transform profile images with arbitrary angles into frontal counterparts. Second, a new training paradigm is proposed to maximize the potential of FSPFM and boost its performance. The latter consists of a pre-training and an attention-guided fine-tuning stage. Moreover, extensive experiments have been conducted on five popular face recognition benchmarks. Results show that not only our method outperforms the state-of-the-art in the pose-invariant face recognition task but also maintains superior performance in other standard scenarios.
Abstract (translated)
无姿态不变的人脸识别已成为现代基于AI的人脸识别系统面临的挑战性问题。该任务旨在将野外采集的侧脸与数据库中注册的正脸进行匹配。现有的方法通过生成模型或学习鲁棒的姿态特征表示来进行人脸正面化。本文提出了一种新的方法，在特征空间内执行人脸正面化和识别。首先，提出了一个新颖的特征空间姿态正面化模块（FSPFM），将任意角度的侧脸图像转换为正脸对应物。其次，提出了一种新的训练范式来最大化FSPFM的潜力并提升其性能。后者包括预训练和注意力引导微调两个阶段。此外，在五个流行的人脸识别基准数据集上进行了广泛的实验。结果显示，不仅我们提出的方法在无姿态不变人脸识别人脸任务中优于现有最佳方法，并且在其他标准场景中也保持了优越的性能。
URL
https://arxiv.org/abs/2505.16412
