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Identity verification based on haptic handwritten signatures: genetic programming with unbalanced data
Author(s):
Affiliation:
Information and Communication Technologies; National Research Council Canada
Language:
English
Type:
Conference publication
Conference:
2012 IEEE Symposium on Computational Intelligence for Security and Defence Applications (CISDA), July 11-13, 2012, Ottawa, Ontario, Canada
Proceedings
Title:
(CISDA 2012): Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defence Applications
Date:
2012
Pages :
1-7
NPArC #:
20794318
Keywords:
Haptics; Biometrics; Genetic Programming; user verification; classification
Abstract:
In 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.
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