Fingerprint minutiae extraction from skeletonized binary images

Farina, Alessandro; Kovács-Vajna, Zsolt Miklós; Leone, A.

doi:10.1016/s0031-3203(98)00107-1

Cited by 120 publications

(44 citation statements)

References 10 publications

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“…In Farina et al (1999), these structures and others were removed from the skeleton image. Minutiae were also categorised or ranked according to the degree of their meeting defined topological rules.…”

Section: Base Theory 21 Minutiae Extractionmentioning

confidence: 99%

Fingerprint Matching using A Hybrid Shape and Orientation Descriptor

Kwan¹,

Gao²

2011

State of the Art in Biometrics

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Section: Base Theory 21 Minutiae Extractionmentioning

confidence: 99%

Fingerprint Matching using A Hybrid Shape and Orientation Descriptor

Kwan¹,

Gao²

2011

State of the Art in Biometrics

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“…These steps include segmentation [1], enhancement [2], extraction [3], matching [4], etc. However, there are some problems in this method.…”

Section: Introductionmentioning

confidence: 99%

A framework of multitemplate ensemble for fingerprint verification

Yin

Ning

Ren

et al. 2012

EURASIP J. Adv. Signal Process.

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How to improve performance of an automatic fingerprint verification system (AFVS) is always a big challenge in biometric verification field. Recently, it becomes popular to improve the performance of AFVS using ensemble learning approach to fuse related information of fingerprints. In this article, we propose a novel framework of fingerprint verification which is based on the multitemplate ensemble method. This framework is consisted of three stages. In the first stage, enrollment stage, we adopt an effective template selection method to select those fingerprints which best represent a finger, and then, a polyhedron is created by the matching results of multiple template fingerprints and a virtual centroid of the polyhedron is given. In the second stage, verification stage, we measure the distance between the centroid of the polyhedron and a query image. In the final stage, a fusion rule is used to choose a proper distance from a distance set. The experimental results on the FVC2004 database prove the improvement on the effectiveness of the new framework in fingerprint verification. With a minutiae-based matching method, the average EER of four databases in FVC2004 drops from 10.85 to 0.88, and with a ridge-based matching method, the average EER of these four databases also decreases from 14.58 to 2.51.

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“…For the purpose of access control, the goal of recognition is (a) to identify correctly a system user from the input fingerprint and grant him or her an appropriate access and (b) to reject non-users or intruders. Various techniques including minutiae-based approaches [7] and texturebased approaches [8] have been applied to fingerprint recognition. Among these techniques, the approach involving the transformation of a fingerprint into a fingercode [8] has received much attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of an Automatic Fingerprint Identification System Using a Genetic Algorithm and Genetic Programming

Wetcharaporn

Chaiyaratana

Huvanandana³

2006

Lecture Notes in Computer Science

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This paper presents the use of a genetic algorithm and genetic programming for the enhancement of an automatic fingerprint identification system (AFIS). The recognition engine within the original system functions by transforming the input fingerprint into a feature vector or fingercode using a Gabor filter bank and attempting to create the best match between the input fingercode and the database fingercodes. A decision to either accept or reject the input fingerprint is then carried out based upon whether the norm of the difference between the input fingercode and the best-matching database fingercode is within the threshold or not. The efficacy of the system is in general determined from the combined true acceptance and true rejection rates. In this investigation, a genetic algorithm is applied during the pruning of the fingercode while the search by genetic programming is executed for the purpose of creating a mathematical function that can be used as an alternative to the norm operator. The results indicate that with the use of both genetic algorithm and genetic programming the system performance has improved significantly.

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Fingerprint minutiae extraction from skeletonized binary images

Cited by 120 publications

References 10 publications

Fingerprint Matching using A Hybrid Shape and Orientation Descriptor

Fingerprint Matching using A Hybrid Shape and Orientation Descriptor

A framework of multitemplate ensemble for fingerprint verification

Enhancement of an Automatic Fingerprint Identification System Using a Genetic Algorithm and Genetic Programming

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