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Math. Comput. Appl. 1999, 4(2), 91-97; doi:10.3390/mca4020091

On the Principal Component Based Fingerprint Classification Using Directional Images

1
Tubitak-MRC-ITRI-Intelligent Systems Group. P.K. 21 41470 Gebze/Kocaeli, Turkey
2
Bogazici University, Comp. Eng. Dep. Bebek 80815 Istanbul, Turkey
*
Authors to whom correspondence should be addressed.
Published: 1 August 1999
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Abstract

This study presents a method for fingerprint recognition based on principal component analysis (PCA) and point patterns (minutae) obtained from the directional histograms of a fingerprint. We first employ Principal Component Analysis (PCA) method to compress fingerprint data. The compressed data are then used for directional image representation. After the compressed data are obtained, the process continues with directional image formation, directional image block representation, and fingerprint matching, respectively. Our method determines the direction of each pixel, process the images in blocks and uses directional histograms thus removes the need for thinning. The method gives the same performance as that of the uncompressed data, but reduces computation. Furthermore, the parts of the system that successfully use artificial neural networks (ANN) are mentioned.
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Ballan, M.; Gurgen, F. On the Principal Component Based Fingerprint Classification Using Directional Images. Math. Comput. Appl. 1999, 4, 91-97.

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Math. Comput. Appl. EISSN 2297-8747 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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