Identity verification based on haptic handwritten signatures: genetic programming with unbalanced data

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DOIResolve DOI: http://doi.org/10.1109/CISDA.2012.6291531
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TypeArticle
Proceedings title(CISDA 2012): Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defence Applications
Conference2012 IEEE Symposium on Computational Intelligence for Security and Defence Applications (CISDA), July 11-13, 2012, Ottawa, Ontario, Canada
ISBN978-1-4673-1416-9
Pages17; # of pages: 7
SubjectHaptics; Biometrics; Genetic Programming; user verification; classification
AbstractIn this paper, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. The relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification is investigated. In particular, several fitness functions are used and their comparative performance is investigated. They take into account the unbalance dataset problem (large disparities within the class distribution), which is present in identity verification scenarios. GP classifiers using such fitness functions compare favorably with classical methods. In addition, they lead to simple equations using a much smaller number of attributes. It was found that collectively, haptic features were approximately as equally important as visual features from the point of view of their contribution to the identity verification process.
Publication date
PublisherIEEE
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number20794318
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Record identifier796e046d-8a93-4645-902e-903f132c5dbd
Record created2012-10-12
Record modified2016-05-09
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