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Fingerprint Classification Using a Feedback-Based Line Detector

Shah, Shesha and Sastry, PS (2004) Fingerprint Classification Using a Feedback-Based Line Detector. In: IEEE Transactions on Systems, Man and Cybernetics, Part B, 34 (1). pp. 85-94.

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Abstract

We present a fingerprint classification algorithm in this paper. This algorithm classifies a fingerprint image into one of the five classes: arch, left loop, right loop, whorl, and tented arch. We use a new low-dimensional feature vector obtained from the output of a novel oriented line detector. Our line detector is a co-operative dynamical system that gives oriented lines and preserves multiple orientations at points where differently oriented lines meet. Our feature extraction process is based on characterizing the distribution of orientations around the fingerprint. We discuss three different classifiers: support vector machines, nearest-neighbor classifier, and neural network classifier. We present results obtained on a National Institute of Standards and Technology (NIST) fingerprint database and compare with other published results on NIST databases. All our classifiers perform equally well, and this suggests that our novel line detection and feature extraction process indeed captures all the crucial information needed for classification in this problem.

Item Type: Journal Article
Additional Information: �©1990 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: Biometrics;Feedback-based line detection;Fingerprint Classification;Neural network classifiers;Support vector machines
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 30 Nov 2005
Last Modified: 19 Sep 2010 04:21
URI: http://eprints.iisc.ernet.in/id/eprint/4224

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