Robust Fingerprint Verification Using M-Triplets

Medina‐Pérez, Miguel Angel; García-Borroto, Milton; Gutierrez-Rodríguez, Andres E.; Altamirano, Leopoldo

doi:10.1109/ichb.2011.6094348

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…The algorithms evaluated are: MCC [4,5,10] (SDK v1.4 available in http://biolab.csr.unibo.it/ mccsdk.html), M3gl [20], JY [15], TK [30], PN [24], QYW [25], MJY [21], MTK [22], MPN [19], and MQYW [21]. All the algorithms are evaluated with their default parameters except for algorithm M3gl, whose parameter c (i.e.…”

Section: Resultsmentioning

confidence: 99%

Introducing an Experimental Framework in C# for Fingerprint Recognition

Medina‐Pérez

Loyola‐González

Gutierrez-Rodríguez

et al. 2014

Lecture Notes in Computer Science

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Abstract. In this paper, a framework for fingerprint recognition is introduced. The framework contains several algorithms for fingerprint matching and feature extraction, as well as the evaluation protocol for several fingerprint verification competitions. All the algorithms are implemented in C# providing the source code which researchers can review, reuse or modify. In order to show the framework relevance, an experimental comparison among different matching algorithms is presented.

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Section: Resultsmentioning

confidence: 99%

Introducing an Experimental Framework in C# for Fingerprint Recognition

Medina‐Pérez

Loyola‐González

Gutierrez-Rodríguez

et al. 2014

Lecture Notes in Computer Science

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“…Fingerprint feature representations have been classified based on different criteria by some authors [13], [26], [35], [47], [48]. According to Vij and Namboodiri [48], fingerprint feature representations are global, local, combined, or transformation-based.…”

Section: A Fingerprint Feature Representations a General Overviewmentioning

confidence: 99%

“…A minutia descriptor is a combination of the minutia representation, which includes its coordinate and angle from the origin of the image (ISO/IEC 19794-2:2005), with additional information about other neighboring features [22], [23], [50]. For example, the orientation-based minutia descriptor [49] incorporates information about the ridge orientation in a neighborhood around a minutia, while the m-triplet [26], [51] incorporates information about the distance and the angles between three neighboring minutiae.…”

Section: A Fingerprint Feature Representations a General Overviewmentioning

confidence: 99%

A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation

et al. 2019

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Latent fingerprint identification is attracting increasing interest because of its important role in law enforcement. Although the use of various fingerprint features might be required for successful latent fingerprint identification, methods based on minutiae are often readily applicable and commonly outperform other methods. However, as many fingerprint feature representations exist, we sought to determine if the selection of feature representation has an impact on the performance of automated fingerprint identification systems. In this paper, we review the most prominent fingerprint feature representations reported in the literature, identify trends in fingerprint feature representation, and observe that representations designed for verification are commonly used in latent fingerprint identification. We aim to evaluate the performance of the most popular fingerprint feature representations over a common latent fingerprint database. Therefore, we introduce and apply a protocol that evaluates minutia descriptors for latent fingerprint identification in terms of the identification rate plotted in the cumulative match characteristic (CMC) curve. From our experiments, we found that all the evaluated minutia descriptors obtained identification rates lower than 10% for Rank−1 and 24% for Rank−100 comparing the minutiae in the database NIST SD27, illustrating the need of new minutia descriptors for latent fingerprint identification.INDEX TERMS Latent fingerprint identification, minutia descriptor, fingerprint feature representation, minutia descriptor evaluation.

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“…2π). From this cylinder, it is possible to find its neighbouring minutiae of which the information used to form the characteristics of this minutia is the position (Chikkerur, Cartwright, & Govindaraju, 2006;Medina-Pérez, García-Borroto, Gutierrez-Rodriguez, & Altamirano-Robles, 2011), ridge (Feng, Ouyang, & Cai, 2006;Wang, Li, & Niu, 2007), orientation (Qi, Yang, & Wang, 2005;Tico & Kuosmanen, 2003), or a combination of these (Feng, 2008).…”

Section: Minutia Cylinder-codementioning

confidence: 99%

Speeding up and enhancing a large-scale fingerprint identification system on GPU

Nguyen

2017

Journal of Information and Telecommunication

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Fingerprint identification is one of the most common biometric feature problems which is used in many applications. Although state-of-the-art algorithms are very accurate, the need for fast processing a big database of millions of fingerprints is highly demanding. In this paper, we propose to adapt the fingerprint matching algorithm based on Minutia Cylinder-Code on Graphics Processing Units to speed up the matching. Another contribution of this paper is to add a consolidation stage after matching to enhance the precision. The experimental results on a GTX-680 and K40 tesla devices with standard data-sets prove that the proposed algorithm can be comparable with the state-of-the-art approach, and is suitable for a real-time identification application. ARTICLE HISTORY

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Robust Fingerprint Verification Using M-Triplets

Cited by 8 publications

References 22 publications

Introducing an Experimental Framework in C# for Fingerprint Recognition

Introducing an Experimental Framework in C# for Fingerprint Recognition

A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation

Speeding up and enhancing a large-scale fingerprint identification system on GPU

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