提出DeepAFRNet模型，精准识别被故意修改的指纹。
Innovative Deep Learning Architecture for Enhanced Altered Fingerprint Recognition
- 基于VGG16提取特征，用余弦相似度匹配指纹嵌入。
- 在三个难度级别下准确率达96.7%至99.54%。
- 使用真实修改指纹数据，适合安全场景部署。
伪造指纹识别（AFR）在边境管控、刑侦及财政准入等场景中极具挑战性，因攻击者可故意修改纹路以逃避检测，因此需具备鲁棒性的识别能力。本文提出DeepAFRNet，一种深度学习识别模型，用于匹配与识别扭曲的指纹样本。该方法采用VGG16作为主干网络提取高维特征，并利用余弦相似度比较嵌入向量。在SOCOFing Real-Altered子集上，针对三个难度等级（Easy、Medium、Hard）进行评估，严苛阈值下准确率分别达到96.7%、98.76%和99.54%。阈值敏感性分析显示，当阈值从0.92放宽至0.72时，准确率骤降至7.86%、27.05%和29.51%，凸显阈值选择在生物识别系统中的关键作用。通过使用真实改造样本并报告分级别指标，DeepAFRNet克服了以往依赖合成篡改或验证协议受限的研究局限，表明其已具备实际应用潜力，尤其适用于对安全性与识别鲁棒性均有高要求的场景。
原文摘要 · Abstract (English)
Altered fingerprint recognition (AFR) is challenging for biometric verification in applications such as border control, forensics, and fiscal admission. Adversaries can deliberately modify ridge patterns to evade detection, so robust recognition of altered prints is essential. We present DeepAFRNet, a deep learning recognition model that matches and recognizes distorted fingerprint samples. The approach uses a VGG16 backbone to extract high-dimensional features and cosine similarity to compare embeddings. We evaluate on the SOCOFing Real-Altered subset with three difficulty levels (Easy, Medium, Hard). With strict thresholds, DeepAFRNet achieves accuracies of 96.7 percent, 98.76 percent, and 99.54 percent for the three levels. A threshold-sensitivity study shows that relaxing the threshold from 0.92 to 0.72 sharply degrades accuracy to 7.86 percent, 27.05 percent, and 29.51 percent, underscoring the importance of threshold selection in biometric systems. By using real altered samples and reporting per-level metrics, DeepAFRNet addresses limitations of prior work based on synthetic alterations or limited verification protocols, and indicates readiness for real-world deployments where both security and recognition resilience are critical.
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