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

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DOIResolve DOI: http://doi.org/10.1145/2457450.2457453
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TypeArticle
Journal titleACM Transactions on Multimedia Computing, Communications and Applications
ISSN1551-6857
Volume9
Issue2
Article number11
SubjectHaptics; Biometrics; Genetic Programming; user verification; classification
AbstractIn 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, naive 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).
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); Information and Communication Technologies
Peer reviewedYes
NPARC number21269645
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Record identifier1451a431-40fb-496b-9d33-982b0b47a0cc
Record created2013-12-13
Record modified2016-05-09
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