标题： 基于潜在扩散的退化条件下法医人脸识别面部增强 显示英文标题

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

摘要： 面部识别系统在处理低质量的法医证据图像时会经历严重的性能下降。 本文对基于潜在扩散的增强方法进行了评估，以在法医相关的退化条件下提高面部识别效果。 使用来自LFW的3000个人的数据库，共24000次识别尝试，我们实施了Flux.1 Kontext Dev流程，并采用Facezoom LoRA适应来测试七种退化类别，包括压缩伪影、模糊效果和噪声污染。 我们的方法表现出显著的改进，将整体识别准确率从29.1%提高到84.5%（提高了55.4个百分点，95%置信区间：[54.1, 56.7]）。 统计分析显示，在所有退化类型中都取得了显著的性能提升，效应量超过了实际显著性的传统阈值。 这些发现确立了基于复杂扩散的增强方法在法医面部识别应用中的潜力。 显示英文摘要

摘要： 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.