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Improving Automated Latent Fingerprint Identification Using Extended Minutia Types
School of Electronic Engineering, Dublin City University, Ireland.
School of Engineering, Universidad Autonoma de Madrid, Spain.
School of Engineering, Universidad Autonoma de Madrid, Spain.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-1400-346X
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2019 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 50, p. 9-19Article in journal (Refereed) Published
Abstract [en]

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. © 2018 Elsevier B.V.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2019. Vol. 50, p. 9-19
Keywords [en]
Latent Fingerprints, Forensics, Extended Feature Sets, Rare minutiae features
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-38113DOI: 10.1016/j.inffus.2018.10.001Scopus ID: 2-s2.0-85054739072OAI: oai:DiVA.org:hh-38113DiVA, id: diva2:1254167
Projects
BBfor2
Funder
EU, FP7, Seventh Framework Programme, FP7-ITN-238803Knowledge Foundation, SIDUS-AIRKnowledge Foundation, CAISR
Note

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 485 project and the CAISR program in Sweden.

Available from: 2018-10-08 Created: 2018-10-08 Last updated: 2019-04-10Bibliographically approved

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Alonso-Fernandez, FernandoBigun, Josef

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