An ensemble of fingerprint matching algorithms based on cylinder codes and mtriplets for latent fingerprint identificati
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ORIGINAL ARTICLE
An ensemble of fingerprint matching algorithms based on cylinder codes and mtriplets for latent fingerprint identification Danilo Valdes‑Ramirez1,2 · Miguel A. Medina‑Pérez1 · Raúl Monroy1 Received: 5 February 2020 / Accepted: 5 September 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Automatic latent fingerprint identification is beneficial during forensic investigations. Usually, latent fingerprint identification algorithms are used to find a subset of similar fingerprints from those previously captured on databases, which are finally examined by latent examiners. Yet, the identification rate achieved by latent fingerprint identification algorithms is far from those obtained by latent examiners. One approach for improving identification rates is the fusion of the match scores computed with fingerprint matching algorithms using a supervised classification algorithm. This approach fuses the results provided by different lower-level algorithms to improve them. Thus, we propose a fusion of fingerprint matching algorithms using a supervised classifier. Our proposal starts with two different local matching algorithms. We substitute their global matching algorithms with another independent of the local matching, creating two lower-level algorithms for fingerprint matching. Then, we combine the output of these lower-level algorithms using a supervised classifier. Our proposal achieves higher identification rates than each lower-level algorithm and their fusion using traditional approaches for most of the rank values and reference databases. Moreover, our fusion algorithm reaches a Rank-1 identification rate of 74.03% and 71.32% matching the 258 samples in the NIST SD27 database against 29,257 and 100,000 references, the two largest reference databases employed in our experiments. Keywords Latent fingerprint identification · Match-score fusion · Fingerprint matching algorithm
1 Introduction Fingerprint matching algorithms are at the core of fingerprint verification and latent fingerprint identification. Fingerprint verification performs a one-to-one comparison to verify a claimed identity with a previously captured fingerprint [19, 24]. Contrarily, latent fingerprint identification performs a one-to-many comparison searching for the most similar fingerprints, enrolled in a reference database, to a * Danilo Valdes‑Ramirez [email protected]; [email protected] Miguel A. Medina‑Pérez [email protected] Raúl Monroy [email protected] 1
Tecnologico de Monterrey, School of Science and Engineering, Carretera al Lago de Guadalupe Km. 3.5, 52926 Atizapán de Zaragoza, Estado deMéxico, Mexico
Department of Computer Sciences, Universidad de Ciego de Ávila, 65100 Ciego de Ávila, Cuba
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latent fingerprint [17, 24]. Most of the fingerprint matching algorithms have been developed for fingerprint verification [36]. As a consequence, fingerprint verification algorithms have reported a high accuracy (see the results in the Fingerprint Verification Competition FVC-onGoing [7]), while latent f
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