Latent Diffusion Based Face Enhancement under Degraded Conditions for Forensic Face Recognition

TL;DR

This study evaluates a latent diffusion-based face enhancement method that significantly improves recognition accuracy on degraded forensic images, demonstrating its potential for practical forensic applications.

Contribution

The paper introduces a novel application of latent diffusion models for face enhancement in forensic imagery, showing substantial accuracy improvements across various degradation types.

Findings

Recognition accuracy increased from 29.1% to 84.5%.

Significant performance gains across all degradation categories.

Effect sizes indicate practical significance of improvements.

Abstract

Face recognition systems experience severe performance degradation when processing low-quality forensic evidence imagery. This paper presents an evaluation of latent diffusion-based enhancement for improving face recognition under forensically relevant degradations. Using a dataset of 3,000 individuals from LFW with 24,000 recognition attempts, we implement the Flux.1 Kontext Dev pipeline with Facezoom LoRA adaptation to test against seven degradation categories, including compression artefacts, blur effects, and noise contamination. Our approach demonstrates substantial improvements, increasing overall recognition accuracy from 29.1% to 84.5% (55.4 percentage point improvement, 95% CI: [54.1, 56.7]). Statistical analysis reveals significant performance gains across all degradation types, with effect sizes exceeding conventional thresholds for practical significance. These findings establish the potential of sophisticated diffusion based enhancement in forensic face recognition applications.