Unified open framework improves automated fingermark identification and analysis
AFID: A Unified Open Framework for Automated Fingermark Identification, Quality Assessment and Feature Extraction
Computer Vision and Pattern Recognition
Summary
Fingermarks, or fingerprints left on surfaces, are important for forensic investigations, but current computer tools to analyze them are often closed-source and trained on private data. The authors developed AFID, an open-source system that uses one shared method to identify fingermarks, assess their quality, and extract features that are useful for matching. This system performs better than earlier methods on standard test sets and works well without complicated image processing steps. By sharing the code and models openly, the authors aim to help others advance fingermark analysis research.
fingermark identificationforensic investigationopen-source frameworkfeature extractionquality assessmentfixed-length representationimage processingaugmentationencoderrank-1 identification
Authors
Tim Oblak, Rudolf Haraksim, Peter Peer
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.