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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
Keywords
Latent Fingerprints, Forensics, Extended Feature Sets, Rare minutiae features
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-38113 (URN)10.1016/j.inffus.2018.10.001 (DOI)2-s2.0-85054739072 (Scopus ID)
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.
2018-10-082018-10-082019-04-10Bibliographically approved