mlsanityData to 7 Oct 2026

Does Head Pose Correction Improve Biometric Facial Recognition?

arXiv 2512.03199 ↗First posted 2 Dec 2025cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 2 Dec 2025.

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References · 17

  1. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  2. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,618
  3. 2014Learning Face Representation from Scratchcited by 451
  4. 2022AdaFace: Quality Adaptive Margin for Face Recognitioncited by 390
  5. 2024The Faiss librarycited by 370
  6. 2018Deep Face Recognition: A Surveycited by 275
  7. 2022Towards Robust Blind Face Restoration with Codebook Lookup Transformercited by 230
  8. 2015The MegaFace Benchmark: 1 Million Faces for Recognition at Scalecited by 199
  9. 2020Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditingcited by 89
  10. 2017Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesiscited by 75
  11. 2018Low-Resolution Face Recognitioncited by 64
  12. 2017Towards Large-Pose Face Frontalization in the Wildcited by 50
5 more
  1. 2021Towards High Fidelity Monocular Face Reconstruction with Rich Reflectance using Self-supervised Learning and Ray Tracingcited by 48
  2. 2021Practical Face Reconstruction via Differentiable Ray Tracingcited by 35
  3. 2014Effective Face Frontalization in Unconstrained Imagescited by 34
  4. 2024Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumventioncited by 21
  5. 2023An Evaluation of Forensic Facial Recognitioncited by 2

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