Skip to Main content Skip to Navigation
Conference papers

Palmprint Classification Using Wavelets and AdaBoost

G. Chen 1 W. Zhu 2 Balázs Kégl 3, 4, 5 Róbert Busa-Fekete 3, 4
1 Department of Mathematics and Statistics
2 ECE - Electrical and Computer Engineering Department [Concordia]
3 LAL - Laboratoire de l'Accélérateur Linéaire
4 LRI - Laboratoire de Recherche en Informatique
5 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
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.
Keywords : Palmprint classification wavelet transform feature extraction AdaBoost
Document type :
Conference papers
Complete list of metadata
http://hal.in2p3.fr/in2p3-00957894
Contributor : Sabine Starita Connect in order to contact the contributor
Submitted on : Tuesday, March 11, 2014 - 11:44:27 AM
Last modification on : Thursday, July 8, 2021 - 3:47:42 AM

Identifiers

  • HAL Id : in2p3-00957894, version 1

Collections

IN2P3 | LAL | CNRS | INRIA | UMR8623 | LRI-AO | EC-PARIS

Citation

G. Chen, W. Zhu, Balázs Kégl, Róbert Busa-Fekete. Palmprint Classification Using Wavelets and AdaBoost. 7th International Symposium on Neural Networks, Jun 2010, Shanghai, China. pp.178-183. ⟨in2p3-00957894⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Les métriques sont temporairement indisponibles