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Does Head Pose Correction Improve Biometric Facial Recognition?

Justin Norman, Hany Farid

TL;DR

The paper evaluates whether head-pose correction and image restoration can meaningfully improve forensic facial recognition. It shows that naive frontalization with NextFace or CFR-GAN degrades performance, but a selective two-stage CFR-GAN + CodeFormer restoration can yield tangible gains when guided by a high-precision failure-prediction model. Through a large-scale, model-agnostic lineup framework using real and synthetic data, the authors quantify when and how restoration helps and highlight the importance of context-aware deployment and transparent reporting. The work provides a framework for evaluating enhancement tools in high-stakes biometric pipelines and emphasizes cautious, evidence-based integration rather than universal application.

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.

Does Head Pose Correction Improve Biometric Facial Recognition?

TL;DR

The paper evaluates whether head-pose correction and image restoration can meaningfully improve forensic facial recognition. It shows that naive frontalization with NextFace or CFR-GAN degrades performance, but a selective two-stage CFR-GAN + CodeFormer restoration can yield tangible gains when guided by a high-precision failure-prediction model. Through a large-scale, model-agnostic lineup framework using real and synthetic data, the authors quantify when and how restoration helps and highlight the importance of context-aware deployment and transparent reporting. The work provides a framework for evaluating enhancement tools in high-stakes biometric pipelines and emphasizes cautious, evidence-based integration rather than universal application.

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.

Paper Structure

This paper contains 17 sections, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Forensic Facial Lineups
  3. Dataset
  4. Creating Lineups
  5. Head-Pose Correction
  6. 3D Head Pose Correction (NextFace)
  7. 2D Head Pose Correction (CFR-GAN)
  8. Lineup Accuracy after Head-Pose Correction
  9. 2D Restoration (CodeFormer)
  10. Lineup Accuracy after Head-Pose Correction and Restoration
  11. Predicting Failure
  12. Dataset
  13. Feature Engineering
  14. Model Architecture and Training
  15. Model Performance
  16. ...and 2 more sections

Figures (4)

  • Figure 1: Facial recognition lineup examples showing one successful match (top row) and four failures. Each row displays a source image (left) followed by the constructed lineup. Green borders indicate successful recognition where the probe with the same identity as the soruce is correctly ranked first; red borders on the probe image indicate that the probe image was not correctly matched.
  • Figure 2: NextFace frontalization.
  • Figure 3: CFR-GAN frontalization.
  • Figure 4: CFR-GAN frontalization followed by CodeFormer (CF) enhancement (top row) and NextFace frontalization followed by CF (bottom row).