修复人脸姿态能提升识别准确率，但方法要选对。
Does Head Pose Correction Improve Biometric Facial Recognition?
- 用3D重建、2D正脸化和特征增强三种方法修复人脸
- 盲目使用会降低识别准确率，但组合使用可提升效果
- 适合做真实场景下的人脸识别优化，尤其在复杂姿态时
生物特征人脸识别模型在处理现实图像时经常因画质差、非正面视角和遮挡导致准确率大幅下降。本文研究了基于AI的头部姿态校正与图像修复是否能提升识别性能。通过一个模型无关的大规模法证评估流程，我们测试了三种修复方法：3D重建（NextFace）、2D正脸化（CFR-GAN）和特征增强（CodeFormer）。结果表明，直接应用这些技术会显著降低识别准确率。然而，选择性地结合CFR-GAN与CodeFormer却能带来明显改善。
原文摘要 · Abstract (English)
Biometric facial recognition models often demonstrate significant decreases in accuracy when processing real-world images, often characterized by poor quality, non-frontal subject poses, and subject occlusions. We investigate whether targeted, AI-driven, head-pose correction and image restoration can improve recognition accuracy. Using a model-agnostic, large-scale, forensic-evaluation pipeline, we assess the impact of three restoration approaches: 3D reconstruction (NextFace), 2D frontalization (CFR-GAN), and feature enhancement (CodeFormer). We find that naive application of these techniques substantially degrades facial recognition accuracy. However, we also find that selective application of CFR-GAN combined with CodeFormer yields meaningful improvements.
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