AFID: A Unified Open Framework for Automated Fingermark Identification, Quality Assessment and Feature Extraction

2026-09-07

AI Summary

为解决自动指纹识别领域封闭源代码和数据问题，本文提出AFID框架，使用公开数据训练统一编码器，实现识别、质量评估和特征提取。

Abstract

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.

Problem

• Automated Fingermark Identification
• Forensic Investigation
• Fragmented Solutions
• Closed-Source
• Private Data

Innovation

• Unified Open Framework
• Single Shared Encoder
• Publicly Available Data
• Heavy Augmentation
• Quality Assessment