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Identity verification based on handwritten signatures with haptic information using genetic programming

Identity verification based on handwritten signatures with haptic information using genetic... Identity Verification Based on Handwritten Signatures with Haptic Information Using Genetic Programming FAWAZ A. ALSULAIMAN and NIZAR SAKR, University of Ottawa ´ JULIO J. VALDES, National Research Council Canada, Institute for Information Technology ABDULMOTALEB EL SADDIK, University of Ottawa In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k-nearest neighbors, na¨ve i Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favorably with the classical methods and use a much fewer number of attributes (with simple function sets). Categories and Subject Descriptors: I.5.1 [Pattern Recognition]: Design Methodology--Classifier design and evaluation; H.5.2 [Information Interfaces and Presentation]: User Interfaces--Haptic I/O General Terms: Algorithms, Measurement, Security Additional Key Words and Phrases: Haptics, Biometrics, Genetic Programming, user verification, classification ACM Reference Format: http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) Association for Computing Machinery

Identity verification based on handwritten signatures with haptic information using genetic programming

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1551-6857
DOI
http://dx.doi.org/10.1145/2457450.2457453
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Abstract

Identity Verification Based on Handwritten Signatures with Haptic Information Using Genetic Programming FAWAZ A. ALSULAIMAN and NIZAR SAKR, University of Ottawa ´ JULIO J. VALDES, National Research Council Canada, Institute for Information Technology ABDULMOTALEB EL SADDIK, University of Ottawa In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k-nearest neighbors, na¨ve i Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favorably with the classical methods and use a much fewer number of attributes (with simple function sets). Categories and Subject Descriptors: I.5.1 [Pattern Recognition]: Design Methodology--Classifier design and evaluation; H.5.2 [Information Interfaces and Presentation]: User Interfaces--Haptic I/O General Terms: Algorithms, Measurement, Security Additional Key Words and Phrases: Haptics, Biometrics, Genetic Programming, user verification, classification ACM Reference Format:

Journal

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) – Association for Computing Machinery

Published: May 1, 2013

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