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arXiv · 2512.03199

Does Head Pose Correction Improve Biometric Facial Recognition?

Justin Norman·Hany Farid
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Abstract

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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Justin Norman, Hany Farid. 2025-12-02. Does Head Pose Correction Improve Biometric Facial Recognition?. https://arxiv.org/abs/2512.03199

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