Fingerprint classification: a review

Yager, Neil; Amin, Adnan

doi:10.1007/s10044-004-0204-7

Cited by 210 publications

(54 citation statements)

References 47 publications

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“…Usually, a matching algorithm does not operate directly on grayscale fingerprint images but requires the derivation of an intermediate fingerprint representation by means of a feature extraction stage. The features that are used for fingerprint representation can be broadly categorized as follows (Yager & Amin, 2004):  features that are global characteristics of a fingerprint such as ridge flow  features that refer to minutiae, such as ridge bifurcations and endings  features that include all dimensional attributes of the ridge such as ridge path deviation, width, shape, pores, edge contours, breaks and scars. The majority of the fingerprint identification systems are minutiae-based, but recently nonminutiae based systems as well as systems that use a combination of the features have been developed.…”

Section: Self-organizing Maps 308mentioning

confidence: 99%

Fingerprint Matching with Self Organizing Maps

Ουζούνογλου

Economopoulos

Asvestas

et al. 2010

XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010

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Section: Self-organizing Maps 308mentioning

confidence: 99%

Fingerprint Matching with Self Organizing Maps

Ουζούνογλου

Economopoulos

Asvestas

et al. 2010

XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010

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“…To minimize the search time and low computational intricacy, and is often used rating of fingerprints to divide the database into smaller subsets [2]. Yager [3] a recent review of the methods of rating of fingerprints. Usually, it is classified fingerprints into five noteworthy classes, known as "Henry classes", specifically Arch, Tented Arch, Whorl, Left Loop and Right Loop.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Feature Extraction Using Convolution and Particle Swarm Optimization Algorithms

Alazzawi¹,

Al-Khalid²,

Alhasnawi³

2017

djps

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Most of the existing fingerprint extraction systems are based on the global features and detailed characteristics of fingerprints, which have a weak performance in cases of poor quality fingerprint images, such as the fingerprint image is incomplete. In order to improve recognition accuracy, reliability and quickness to identify the fingerprints a new trend has been opened by using swarm intelligence techniques in biometric field. Therefore, particle swarm optimization techniques (PSO) are used in this paper to build fingerprints authentication system. A fast fingerprint identification method based on the convolution transformation and Particle Swarm Optimization algorithms proposed. The convolution algorithm was used to extract the convolved feature and then found the optimal solution from this feature by using Particle Swarm Optimization algorithm. Experimental results show that, the proposed method has a high efficiency in extracting features from fingerprints, strong strength, and good accuracy for recognition. Keywords

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“…Most often fingerprint identification systems, for humans or wood, need to address the following design steps (Jain et al, 1997) (Yager and Amin, 2004).…”

Section: Automatic Fingerprint Identification Systemsmentioning

confidence: 99%

Recognition of boards using wood fingerprints based on a fusion of feature detection methods

Pahlberg

Hagman

Thurley

2015

Computers and Electronics in Agriculture

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This paper investigates the possibility to automatically match and recognize individual Scots pine (Pinus sylvestris L.) boards using a fusion of two feature detection methods. The first method denoted Block matching method, detects corners and matches square regions around these corners using a normalized Sum of Squared Differences (SSD) measure. The second method denoted the SURF (Speeded-Up Robust Features) matching method, matches SURF features between images (Bay et al., 2008). The fusion of the two feature detection methods improved the recognition rate of wooden floorboards substantially compared to the individual methods. Perfect matching accuracy was obtained for board pieces with more than 20 knots using high quality images. More than 90% matching accuracy was achieved for board pieces with more than 10 knots, using both high-and low quality images.

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Fingerprint classification: a review

Cited by 210 publications

References 47 publications

Fingerprint Matching with Self Organizing Maps

Fingerprint Matching with Self Organizing Maps

Fingerprint Feature Extraction Using Convolution and Particle Swarm Optimization Algorithms

Recognition of boards using wood fingerprints based on a fusion of feature detection methods

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