Neuware -Fingerprint matching is a widely used biometric authentication system that is done on the basis that every person in the world has its own fingerprint. Every fingerprint has its universal and uniqueness characteristics and widely acceptability. We have mentioned that each fingerprint is formed in the womb during the age 17th week of the fetus and remain unchanged throughout the whole life. In our study we have tried to find out the difference between the input fingerprints by concept of the uniqueness of the fingerprints. For experiment we have taken the concept of matching any two fingerprints from the online journal [8]. We have seen that their input fingerprint, histogram equalization for enhancing the fingerprints, binarization, image segmentation (Block direction estimation and ROI extraction), and false minutia removal, minutia matcher (Alignment stage and matching stage) are discussed. After that we have mentioned an exceptional concept that every image give its own matrix which help to find out the difference between the input fingerprint images.Books on Demand GmbH, �berseering 33, 22297 Hamburg 52 pp. Englisch.

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T�tulo: Fingerprint Matching Through Feature Extraction & Matrix Equalization
Editor: LAP LAMBERT Academic Publishing M�r 2015
Fecha de publicaci�n: 2015
Idioma: Ingl�s
ISBN 10: 3659511714
ISBN 13: 9783659511714
Encuadernaci�n: Taschenbuch
Estado: Neu

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Sinopsis

Fingerprint matching is a widely used biometric authentication system that is done on the basis that every person in the world has its own fingerprint. Every fingerprint has its universal and uniqueness characteristics and widely acceptability. We have mentioned that each fingerprint is formed in the womb during the age 17th week of the fetus and remain unchanged throughout the whole life. In our study we have tried to find out the difference between the input fingerprints by concept of the uniqueness of the fingerprints. For experiment we have taken the concept of matching any two fingerprints from the online journal [8]. We have seen that their input fingerprint, histogram equalization for enhancing the fingerprints, binarization, image segmentation (Block direction estimation and ROI extraction), and false minutia removal, minutia matcher (Alignment stage and matching stage) are discussed. After that we have mentioned an exceptional concept that every image give its own matrix which help to find out the difference between the input fingerprint images.

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