The quality assessment of fingermarks (latent fingerprints) is an essential part of a forensic investigation. It indicates how valuable the fingermarks are as forensic evidence, it determines how they should be further processed, and it correlates with the likelihood of successful identification, i.e., finding a matching fingerprint in a reference database. Since the environments in which fingerprints are found are not controlled, this task proves challenging even with modern machine learning solutions. In this work, we propose a predictive framework for automated fingermark quality assessment (AFQA). With this iteration of AFQA, we bridge the gap between the classic machine learning approach with handcrafted features and the modern deep learning paradigm, evaluate the advantages and disadvantages of these methodologies, and provide the rationale and direction for future development of AFQA methods. We present a significantly improved AFQA toolbox and provide a quality aggregation method capable of fusing together multiple predicted quality values from an ensemble of quality assessment models. The proposed ensemble approach provides improved prediction performance while reducing processing time compared to existing state-of-the-art solutions.
OBLAK Tim;
HARAKSIM Rudolf;
PEER Peter;
BESLAY Laurent;
2023-04-20
ELSEVIER
JRC128494
0950-7051 (online),
https://www.sciencedirect.com/science/article/pii/S0950705122005718,
https://publications.jrc.ec.europa.eu/repository/handle/JRC128494,
10.1016/j.knosys.2022.109148 (online),
This document is only visible at the Commission level.
You are not authorized to publish or distribute it outside the European Commission.
This is a public document. You can share this publication.
Additional supporting files
| File name | Description | File type | |
|---|
