Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/222
Title: Hand-geometry recognition using entropy-based discretization
Authors: Pathak, AK 
Zhang, DD 
Keywords: Biometrics
Feature discretization
Feature representation
Hand geometry
Personal recognition
Issue Date: Jun-2007
Publisher: IEEE
Source: IEEE transactions on information forensics and security, June 2007, v. 2, no. 2, p. 181-187 How to cite?
Journal: IEEE transactions on information forensics and security 
Abstract: The hand-geometry-based recognition systems proposed in the literature have not yet exploited user-specific dependencies in the feature-level representation. We investigate the possibilities to improve the performance of the existing hand-geometry systems using the discretization of extracted features. This paper proposes employing discretization of hand-geometry features, using entropy-based heuristics, to achieve the performance improvement. The performance improvement due to the unsupervised and supervised discretization schemes is compared on a variety of classifiers: k-NN, naïve Bayes, SVM, and FFN. Our experimental results on the database of 100 users achieve significant improvement in the recognition accuracy and confirm the usefulness of discretization in hand-geometry-based systems.
URI: http://hdl.handle.net/10397/222
ISSN: 1556-6013
DOI: 10.1109/TIFS.2007.896915
Rights: © 2007 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.
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