Fingerprint classification

Karu, Kalle; Jain, Anil K.

doi:10.1016/0031-3203(95)00106-9

Cited by 448 publications

(238 citation statements)

References 7 publications

Supporting

Mentioning

235

Contrasting

Order By: Relevance

“…In their 1996 manuscript, Karu and Jain [2] present an example of such a fingerprint classification scheme. Fingerprint translation and fingerprint rotation are equally problematic, however they cannot be fully exploited in the same discussion.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fingerprint Re-alignment Solution that Uses the TFCP as a Reference

Msiza¹,

Malumedzha²,

Leke-Betechuoh³

2011

IJMLC

View full text Add to dashboard Cite

Abstract-Fingerprint re-alignment is an important aspect in the subject of fingerprint biometrics because a fingerprint recognition system that does not attempt to re-align an involuntarily rotated fingerprint can create opportunities for falsely rejecting a legitimate subject. It can also create opportunities for security attacks where an illegitimate subject voluntarily rotates their fingerprint, as a way of tempering with the functionality of the recognition system. This manuscript presents a new fingerprint re-alignment solution that uses the True Fingerprint Center Point (TFCP) as a reference, where the TFCP is introduced as the actual center of the fingerprint image foreground. Using the TFCP as a reference point proves -in general -to be fast, independent, and efficient for all types of fingerprints. It, in particular, is useful when dealing with the type of fingerprints that belong to the Plain Arch (PA) fingerprint class. The credibility of this proposed solution is interrogated through its applicability on fingerprint ridge surface estimation and fingerprint template matching.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Fingerprint Re-alignment Solution that Uses the TFCP as a Reference

Msiza¹,

Malumedzha²,

Leke-Betechuoh³

2011

IJMLC

View full text Add to dashboard Cite

show abstract

“…The purpose of classification is to cluster a database of fingerprints into sub-categories where the sub-categories are in general defined according to a Henry system [2]. Several techniques including syntactic approaches [3], structural approaches [4], neural network approaches [5] and statistical approaches [6] have been successfully used in fingerprint classification. In contrast, the purpose of recognition is to match the fingerprint of interest to the identity of an individual.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Local orientational flows are estimated from a fingerprint image and desired features are computed from the estimated orientation field. The features can be singular points and the classification is based on the number and locations of detected singular points [8]. In [9], a directional image is partitioned into homogeneous regions and the relative relations of segmented regions are used for classification.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis

Park

2005

Pattern Recognition

View full text Add to dashboard Cite

In this paper, we present a new approach for fingerprint classification based on Discrete Fourier Transform (DFT) and nonlinear discriminant analysis. Utilizing the Discrete Fourier Transform and directional filters, a reliable and efficient directional image is constructed from each fingerprint image, and then nonlinear discriminant analysis is applied to the constructed directional images, reducing the dimension dramatically and extracting the discriminant features. The proposed method explores the capability of DFT and directional filtering in dealing with low quality images and the effectiveness of nonlinear feature extraction method in fingerprint classification. Experimental results demonstrates competitive performance compared with other published results.

show abstract

Fingerprint classification

Cited by 448 publications

References 7 publications

A Novel Fingerprint Re-alignment Solution that Uses the TFCP as a Reference

A Novel Fingerprint Re-alignment Solution that Uses the TFCP as a Reference

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

Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis

Contact Info

Product

Resources

About