Palmprint Recognition Using Wavelet Based Kernel PCA

Author

Aykut, Murat ; Ekinci, Murat

Author_Institution

Karadeniz Teknik Univ., Trabzon, Turkey

fYear

2007

fDate

11-13 June 2007

Firstpage

1

Lastpage

4

Abstract

This paper presents a wavelet based kernel principal component analysis (KPCA) palmprint recognition method for human identification. The intensity values of palmprint images are first normalized by using their mean and their standard deviation. The normalized images are then transformed to the spectral domain by using wavelet transform and lowest frequencies are selected by filtering. Next, the feature vectors are formed with KPCA method which divergences samples on the nonlinear space. Finally, weighted Euclidean distance based nearest neighbor method is realized for palmprint classification. Experiments are performed on the most-well known public palmprint database, PolyU, includes 600 samples of 100 different persons.

Keywords

biometrics (access control); filtering theory; image classification; principal component analysis; vectors; wavelet transforms; PolyU; feature vectors; human identification; image filtering; kernel principal component analysis; nearest neighbor method; palmprint classification; palmprint recognition; public palmprint database; wavelet transform; weighted Euclidean distance; Euclidean distance; Filtering; Frequency; Humans; Kernel; Nearest neighbor searches; Principal component analysis; Wavelet analysis; Wavelet domain; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications, 2007. SIU 2007. IEEE 15th

Conference_Location

Eskisehir

Print_ISBN

1-4244-0719-2

Electronic_ISBN

1-4244-0720-6

Type

conf

DOI

10.1109/SIU.2007.4298606

Filename

4298606