DocumentCode :
651426
Title :
Identity verification based on haptic handwritten Signature: Novel fitness functions for GP framework
Author :
Alsulaiman, Fawaz A. ; Valdes, Julio J. ; El Saddik, Abdulmotaleb
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci. (EECS), Univ. of Ottawa, Ottawa, ON, Canada
fYear :
2013
fDate :
26-27 Oct. 2013
Firstpage :
98
Lastpage :
102
Abstract :
Fitness functions are the evaluation measures driving evolutionary processes towards solutions. In this paper, three fitness functions are proposed for solving the unbalanced dataset problem in Haptic-based handwritten signatures using genetic programming (GP). The use of these specifically designed fitness functions produced simpler analytical expressions than those obtained with currently available fitness measures, while keeping comparable classification accuracy. The functions introduced in this paper capture explicitly the nature of unbalanced data, exhibit better dimensionality reduction and have better False Rejection Rate.
Keywords :
genetic algorithms; handwriting recognition; haptic interfaces; GP framework; evolutionary processes; false rejection rate; genetic programming; haptic based handwritten signatures; identity verification; novel fitness functions; Accuracy; Educational institutions; Evolutionary computation; Gene expression; Genetic programming; Haptic interfaces; Programming;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Haptic Audio Visual Environments and Games (HAVE), 2013 IEEE International Symposium on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4799-0848-6
Type :
conf
DOI :
10.1109/HAVE.2013.6679618
Filename :
6679618
Link To Document :
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