Improving Automated Latent Fingerprint Identification using Extended Minutia Types
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
ElsevierDate
2019-10Citation
10.1016/j.inffus.2018.10.001
Information Fusion 50 (2019): 9-19
ISSN
1566-2535DOI
10.1016/j.inffus.2018.10.001Funded by
R.K. was supported for the most part of this work by a Marie Curie Fellowship under project BBfor2 from European Commission (FP7-ITN-238803). This work has also been partially supported by Spanish Guardia Civil, and project CogniMetrics (TEC2015-70627-R) from Spanish MINECO/FEDER. The researchers from Halmstad University acknowledge funding from KK-SIDUS-AIR project and the CAISR program in Sweden.Project
Gobierno de españa. TEC2015-70627-R; info:eu-repo/grantAgreement/EC/FP7/238803Editor's Version
https://doi.org/10.1016/j.inffus.2018.10.001Subjects
Latent Fingerprints; Forensics; Extended Feature Sets; Rare minutiae features; TelecomunicacionesRights
© 2018 ElsevierEsta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Abstract
Latent fingerprints are usually processed with Automated Fingerprint Identification Systems (AFIS) by law enforcement agencies to narrow down possible suspects from a criminal database. AFIS do not commonly use all discriminatory features available in fingerprints but typically use only some types of features automatically extracted by a feature extraction algorithm. In this work, we explore ways to improve rank identification accuracies of AFIS when only a partial latent fingerprint is available. Towards solving this challenge, we propose a method that exploits extended fingerprint features (unusual/rare minutiae) not commonly considered in AFIS. This new method can be combined with any existing minutiae-based matcher. We first compute a similarity score based on least squares between latent and tenprint minutiae points, with rare minutiae features as reference points. Then the similarity score of the reference minutiae-based matcher at hand is modified based on a fitting error from the least square similarity stage. We use a realistic forensic fingerprint casework database in our experiments which contains rare minutiae features obtained from Guardia Civil, the Spanish law enforcement agency. Experiments are conducted using three minutiae-based matchers as a reference, namely: NIST-Bozorth3, VeriFinger-SDK and MCC-SDK. We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features
Files in this item
Google Scholar:Krish, Ram P
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Fiérrez Aguilar, Julián
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Ramos Castro, Daniel
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Alonso-Fernández, Fernando
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Bigun, Josef
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