Does Head Pose Correction Improve Biometric Facial Recognition?

• URL: http://arxiv.org/abs/2512.03199v1
• Date: Tue, 02 Dec 2025 19:53:30 GMT
• Title: Does Head Pose Correction Improve Biometric Facial Recognition?
• Authors: Justin Norman, Hany Farid,
• Abstract summary: We investigate whether targeted, AI-driven, head-pose correction and image restoration can improve recognition accuracy.<n>We find that naive application of 3D reconstruction (NextFace), 2D frontalization (CFR-GAN), and feature enhancement (CodeFormer) substantially degrades facial recognition accuracy.<n>However, we also find that selective application of CFR-GAN combined with CodeFormer yields meaningful improvements.
• Score: 14.466802614938333
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Do Not DeepFake Me: Privacy-Preserving Neural 3D Head Reconstruction Without Sensitive Images [5.462031439048112]
  We propose a novel two-stage 3D facial reconstruction method aimed at avoiding exposure to sensitive facial information while preserving detailed geometric accuracy.<n>Our approach first uses non-sensitive rear-head images for initial geometry and then refines this geometry using processed privacy-removed gradient images.
  arXiv  Detail & Related papers  (2023-12-07T07:41:10Z)
• Effective Adapter for Face Recognition in the Wild [72.75516495170199]
  We tackle the challenge of face recognition in the wild, where images often suffer from low quality and real-world distortions. Traditional approaches-either training models directly on degraded images or their enhanced counterparts using face restoration techniques-have proven ineffective. We propose an effective adapter for augmenting existing face recognition models trained on high-quality facial datasets.
  arXiv  Detail & Related papers  (2023-12-04T08:55:46Z)
• NeutrEx: A 3D Quality Component Measure on Facial Expression Neutrality [7.736597471757526]
  We propose a quality measure based on the accumulated distances of a 3D face reconstruction to a neutral expression anchor. Our evaluations demonstrate the superiority of our proposed method compared to baseline approaches.
  arXiv  Detail & Related papers  (2023-08-19T09:38:39Z)
• Boosting Cross-Quality Face Verification using Blind Face Restoration [0.13654846342364302]
  It is crucial for the task of face verification to enhance the perceptual quality of the low quality images. In this paper, we investigate the impact of applying three state-of-the-art blind face restoration techniques on the performance of face verification system.
  arXiv  Detail & Related papers  (2023-08-15T18:05:19Z)
• Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency [5.040145546652933]
  We propose a novel method for anonymised face generation for 3D T1-weighted scans based on a 3D conditional generative adversarial network. The proposed method takes 9 seconds for face generation and is suitable for recovering consistent post-processing results after defacing.
  arXiv  Detail & Related papers  (2023-05-26T13:34:14Z)
• Total Scale: Face-to-Body Detail Reconstruction from Sparse RGBD Sensors [52.38220261632204]
  Flat facial surfaces frequently occur in the PIFu-based reconstruction results. We propose a two-scale PIFu representation to enhance the quality of the reconstructed facial details. Experiments demonstrate the effectiveness of our approach in vivid facial details and deforming body shapes.
  arXiv  Detail & Related papers  (2021-12-03T18:46:49Z)
• Joint Face Image Restoration and Frontalization for Recognition [79.78729632975744]
  In real-world scenarios, many factors may harm face recognition performance, e.g., large pose, bad illumination,low resolution, blur and noise. Previous efforts usually first restore the low-quality faces to high-quality ones and then perform face recognition. We propose an Multi-Degradation Face Restoration model to restore frontalized high-quality faces from the given low-quality ones.
  arXiv  Detail & Related papers  (2021-05-12T03:52:41Z)
• Implicit Subspace Prior Learning for Dual-Blind Face Restoration [66.67059961379923]
  A novel implicit subspace prior learning (ISPL) framework is proposed as a generic solution to dual-blind face restoration. Experimental results demonstrate significant perception-distortion improvement of ISPL against existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-10-12T08:04:24Z)
• Inducing Predictive Uncertainty Estimation for Face Recognition [102.58180557181643]
  We propose a method for generating image quality training data automatically from'mated-pairs' of face images. We use the generated data to train a lightweight Predictive Confidence Network, termed as PCNet, for estimating the confidence score of a face image.
  arXiv  Detail & Related papers  (2020-09-01T17:52:00Z)
• Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Estimation [92.86123832948809]
  We propose a deep face super-resolution (FSR) method with iterative collaboration between two recurrent networks. In each recurrent step, the recovery branch utilizes the prior knowledge of landmarks to yield higher-quality images. A new attentive fusion module is designed to strengthen the guidance of landmark maps.
  arXiv  Detail & Related papers  (2020-03-29T16:04:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.