在特征空间实现任意角度人脸正面化，提升跨姿态人脸识别准确率。
Pose-invariant face recognition via feature-space pose frontalization
- 在特征空间中直接进行人脸正面化，避免生成失真
- 在五个公开数据集上均超越当前最佳方法，识别率显著提升
- 适合需要高鲁棒性的人脸识别系统部署
姿态不变人脸识别已成为现代AI人脸系统面临的挑战性问题，旨在将野外采集的侧脸与数据库中注册的正脸进行匹配。现有方法通过生成模型或学习姿态鲁棒特征表示来实现人脸正面化。本文提出一种在特征空间内完成人脸正面化与识别的新方法：首先设计了新型特征空间姿态正面化模块（FSPFM），可将任意角度的侧脸图像转换为正面图像；其次提出一种新的训练范式，包含预训练与注意力引导微调两个阶段，以充分挖掘FSPFM的潜力。在五个主流人脸识别基准数据集上进行了大量实验，结果表明，该方法不仅在姿态不变人脸识别任务中优于现有最先进水平，且在标准识别场景下也保持优异性能。
原文摘要 · Abstract (English)
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.
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