Palmprint classification using wavelets and adaboost

Authors:
Guangyi Chen;Wei-ping Zhu;Balázs Kégl;Róbert Busa Fekete
Affiliations:
Department of Mathematics and Statistics, Concordia University, Montreal, Quebec, Canada;Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada;LAL/LRI, University of Paris-Sud, CNRS, Orsay, France;LAL/LRI, University of Paris-Sud, CNRS, Orsay, France
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Year:
2010

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Abstract

A new palmprint classification method is proposed in this paper by using the wavelet features and AdaBoost The method outperforms all other classification methods for the PolyU palmprint database The novelty of the method is two-fold On one hand, the combination of wavelet features with AdaBoost has never been proposed for palmprint classification before On the other hand, a recently developed base learner (products of base classifiers) is included in this paper Experiments are conducted in order to show the effectiveness of the proposed method for palmprint classification.