发表机构
University of Ljubljana; Joint Research Centre of the European Commission（卢布尔雅那大学; 欧盟委员会联合研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AFID提出统一开源框架，基于共享编码器实现指纹识别、质量评估与特征提取，仅用公开数据训练，在多个基准上超越商业匹配器，并开源代码与模型。
AI 中文摘要
自动指纹鉴定是法医调查的基础，然而该领域的进展受到碎片化、闭源解决方案的阻碍，这些方案通常基于私有或已停止发布的数据进行训练。我们提出了AFID，一个统一的开放源代码框架，用于摩擦脊图像处理，该框架基于一个共享的编码器执行识别、质量评估和特征提取，且仅使用公开可用的数据进行训练。其核心是一个为身份判别而学习的固定长度表示，并在重度数据增强下进行训练。尽管在推理时除了调整大小和填充外几乎不进行任何预处理，AFID在固定长度指纹识别方面达到了新的最先进水平，在NIST SD 27（67.6% rank-1）、SD 302（54.9% rank-1）和SD 303（67.6% rank-1）数据集上均领先，超越了商业匹配器在指纹上的表现。从相同的冻结骨干网络中，一个质量评估模块能比任何对比基线更准确地预测识别效用，并能在独立的匹配器之间泛化，而轻量级解码器恢复的细节点、脊线方向和分割结果与专用方法相当。该框架证明了一个单一、高效训练的编码器能够支持完整的指纹处理流程，从识别到质量评估直至特征提取。为了进一步加速指纹分析研究，我们将代码、模型和标注数据发布给社区。
英文摘要
Automated fingermark identification is the foundation of forensic investigation, yet progress in the field is held back by fragmented, closed-source solutions trained on private or discontinued data. We present AFID, a unified open-source framework for friction ridge image processing that performs recognition, quality assessment, and feature extraction based on a single shared encoder, trained exclusively on publicly available data. At its core is a fixed-length representation learned for identity discrimination, trained under heavy augmentation. Despite applying essentially no preprocessing beyond resizing and padding at inference, AFID sets a new state of the art in fixed-length fingermark recognition, leading identification across NIST SD 27 (67.6% rank-1) , SD 302 (54.9% rank-1), and SD 303 (67.6% rank-1), surpassing a commercial matcher on fingermarks. From the same frozen backbone, a quality assessment module predicts recognition utility more accurately than any compared baseline and generalizes across independent matchers, while lightweight decoders recover minutiae, ridge orientation, and segmentation competitive with dedicated methods. The framework proves that a single, efficiently trained encoder can support the full fingermark processing pipeline, from recognition through quality assessment all the way to feature extraction. To accelerate research on fingermark analysis even further, we release the code, models, and annotations to the community.
