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.
Abstract (translated)
面部识别系统在处理低质量的法医证据图像时性能会严重下降。本文介绍了一种基于潜扩散增强技术来改善法医学相关退化条件下面部识别精度的评估方法。我们使用包含3000名来自LFW数据集个体和24000次面部识别尝试的数据，实现了带有Facezoom LoRA适配器的Flux.1 Kontext Dev流水线，并针对包括压缩伪影、模糊效果和噪声污染在内的七种退化类型进行了测试。我们的方法展示了显著改进，在所有降级条件下，总体识别精度从29.1%提高到84.5%（提升了55.4个百分点，95%的置信区间为[54.1, 56.7]）。统计分析显示在各种退化类型下均存在显着性能提升，效应大小超过了实用意义的传统阈值。这些发现确立了复杂扩散增强技术在法医面部识别应用中的潜力。
URL
https://arxiv.org/abs/2508.00941
