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AOV Based Fingerprint Minutiae Matching System
matlab-recognition-code.com/aov-based-fingerprint-minutia...
Abstract
Minutia matching is the preferred strategy to fingerprint recognition. In this paper, we analyzed a novel fingerprint function named adjoining orientation vector, or AOV, for fingerprint matching. In the primary stage, AOV is used to seek out potential trivia pairs. Then one trivia set is rotated and translated. This is adopted by a preliminary matching to make sure reliability in addition to a advantageous matching to beat attainable distortion. Such technique has been deployed to a payroll and safety entry info system and its workability is encouraging. The info system goals to supply a extremely secured and automatic identification system for payroll monitoring in addition to approved entry to working areas.
Because of uniqueness, as a private identification technique, fingerprint has been extensively used up to now many years. The hottest matching technique for fingerprint identification is trivia matching. The easiest sample of the trivia-based mostly representations consists of a set of trivia, together with ridge endings and bifurcations outlined by their spatial coordinates. Each minutia is described by its spatial location related to the orientation. Although a set of trivia has been extensively used for matching, the noise drawback in a fingerprint picture has not been solved. The drawback of trivia based mostly technique is the shortage of robustness, there are some various strategies proposed, as an example, Jain’s filterbank technique and Isenor and Zaky’s graph matching technique. The function vector of minutia usually consists of the minutia sort, the coordinates and the tangential angle of the minutia. The automated fingerprint verification/identification is then achieved with a type of level sample matching as an alternative of the fingerprint picture matching. Several level sample matching algorithms have been proposed within the literature. The level sample matching is usually intractable as a result of the correspondences between the 2 level units of template and enter fingerprint are unknown. The minutia correspondences are troublesome to acquire on account of a number of elements such because the rotation, translation and deformation of the fingerprints, the situation and course errors of the detected trivia in addition to the presence of spurious trivia and the absence of real trivia.
This package deal makes use of Peter Kovesi’s code for fingerprint enhancement, “MATLAB and Octave Functions for Computer Vision and Image Processing” and it’s based mostly on the paper “Adjacent orientation vector based mostly fingerprint trivia matching system”, M. R. Ng, X. Tong, X. Tang and A. Shi, Pattern Recognition, ICPR 2004. This article is accessible at citeseer.ist.psu.edu/739574.html.
We have examined the code with Set “A” of FVC2004 Database, utilizing one hundred courses, I fingerprint photographs randomly chosen for coaching (completely I*one hundred photographs) and eight-I fingerprint pictures used for testing (completely 800-I*one hundred photographs), with none overlapping between coaching and testing pictures, acquiring the next outcomes (J is the popularity price):
I = A, J = 88.forty%
I = P, S = seventy nine.forty eight%
I = B, S = 60.fifty nine%
Keyword: Matlab, supply, code, adjoining, orientation, vector, fingerprint, trivia, matching, system, recognition, verification, identification.
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AOV Based Fingerprint Minutiae Matching System
matlab-recognition-code.com/aov-based-fingerprint-minutia...
Abstract
Minutia matching is the preferred strategy to fingerprint recognition. In this paper, we analyzed a novel fingerprint function named adjoining orientation vector, or AOV, for fingerprint matching. In the primary stage, AOV is used to seek out potential trivia pairs. Then one trivia set is rotated and translated. This is adopted by a preliminary matching to make sure reliability in addition to a advantageous matching to beat attainable distortion. Such technique has been deployed to a payroll and safety entry info system and its workability is encouraging. The info system goals to supply a extremely secured and automatic identification system for payroll monitoring in addition to approved entry to working areas.
Because of uniqueness, as a private identification technique, fingerprint has been extensively used up to now many years. The hottest matching technique for fingerprint identification is trivia matching. The easiest sample of the trivia-based mostly representations consists of a set of trivia, together with ridge endings and bifurcations outlined by their spatial coordinates. Each minutia is described by its spatial location related to the orientation. Although a set of trivia has been extensively used for matching, the noise drawback in a fingerprint picture has not been solved. The drawback of trivia based mostly technique is the shortage of robustness, there are some various strategies proposed, as an example, Jain’s filterbank technique and Isenor and Zaky’s graph matching technique. The function vector of minutia usually consists of the minutia sort, the coordinates and the tangential angle of the minutia. The automated fingerprint verification/identification is then achieved with a type of level sample matching as an alternative of the fingerprint picture matching. Several level sample matching algorithms have been proposed within the literature. The level sample matching is usually intractable as a result of the correspondences between the 2 level units of template and enter fingerprint are unknown. The minutia correspondences are troublesome to acquire on account of a number of elements such because the rotation, translation and deformation of the fingerprints, the situation and course errors of the detected trivia in addition to the presence of spurious trivia and the absence of real trivia.
This package deal makes use of Peter Kovesi’s code for fingerprint enhancement, “MATLAB and Octave Functions for Computer Vision and Image Processing” and it’s based mostly on the paper “Adjacent orientation vector based mostly fingerprint trivia matching system”, M. R. Ng, X. Tong, X. Tang and A. Shi, Pattern Recognition, ICPR 2004. This article is accessible at citeseer.ist.psu.edu/739574.html.
We have examined the code with Set “A” of FVC2004 Database, utilizing one hundred courses, I fingerprint photographs randomly chosen for coaching (completely I*one hundred photographs) and eight-I fingerprint pictures used for testing (completely 800-I*one hundred photographs), with none overlapping between coaching and testing pictures, acquiring the next outcomes (J is the popularity price):
I = A, J = 88.forty%
I = P, S = seventy nine.forty eight%
I = B, S = 60.fifty nine%
Keyword: Matlab, supply, code, adjoining, orientation, vector, fingerprint, trivia, matching, system, recognition, verification, identification.
Complete your name and email to Download This .
Click Here For Your Donation In Order To Obtain The Source Code