Palmprint recognition based on modified DCT features and RBF neural network

Author

Yu, Peng-fei ; Xu, Dan

Author_Institution

Sch. of Inf., Yunnan Univ., Kunming

Volume

5

fYear

2008

fDate

12-15 July 2008

Firstpage

2982

Lastpage

2986

Abstract

In this paper, a novel palmprint recognition approach is presented. A modified discrete cosine transform based feature extraction method is used to obtain palmprint features. Furthermore, a radial basis function neural network is employed for palmprint classification. In order to facilitate the training of radial basis function neural network, principal components analysis is applied to reduce these features to a reasonable dimension. The experiment results show that the method is effective.

Keywords

biometrics (access control); discrete cosine transforms; image recognition; principal component analysis; radial basis function networks; RBF neural network; discrete cosine transform; feature extraction method; palmprint classification; palmprint recognition; principal components analysis; radial basis function neural network; Biometrics; Cybernetics; Data mining; Discrete cosine transforms; Feature extraction; Fingerprint recognition; Machine learning; Neural networks; Principal component analysis; Radial basis function networks; Biometrics; DCT-mod2; PCA; Palmprint recognition; RBF neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2008 International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4244-2095-7

Electronic_ISBN

978-1-4244-2096-4

Type

conf

DOI

10.1109/ICMLC.2008.4620918

Filename

4620918