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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, Ajay Kumar
Zhang, David D.
Subjects: Biometrics
Feature discretization
Feature representation
Hand geometry
Personal recognition
Issue Date: Jun-2007
Publisher: IEEE
Citation: IEEE Transactions on information forensics and security, June 2007, v. 2, no. 2, p. 181-187.
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.
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.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/222
ISSN: 15566013
Appears in Collections:COMP Journal/Magazine Articles

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