DocumentCode :
2302919
Title :
Palmprint recognition with applying different kernel matrix sizes on Gabor wavelet features
Author :
Aykut, Murat ; Ekinci, Murat
Author_Institution :
Bilgisayar Muhendisligi Bolumu, Karadeniz Teknik Univ., Trabzon, Turkey
fYear :
2009
fDate :
9-11 April 2009
Firstpage :
249
Lastpage :
252
Abstract :
This paper presents Gabor based Kernel Principal Component Analysis (KPCA) palmprint recognition method for human identification. The intensity values of palmprint images extracted by using an image preprocessing method are first normalized. Then these images are transformed to the spectral domain by using Gabor wavelet transform. The transformed palm images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. Next, the feature vectors are nonlinearly maps into a high dimensional feature space with KPCA method. In this method during kernel matrix calculation, the sample numbers per class changed and it´s effect investigated. Finally, weighted Euclidean distance based nearest neighbor method is realized for classification. The proposed algorithm tested on the most-well known palmprint database, PolyU, includes 7752 samples of 386 different people.
Keywords :
biometrics (access control); feature extraction; image classification; matrix algebra; principal component analysis; spectral analysis; vectors; wavelet transforms; Gabor wavelet feature vector; high dimensional feature space; human identification; image classification; image preprocessing method; kernel matrix; kernel principal component analysis; palmprint image extraction; palmprint recognition method; spectral domain; weighted euclidean distance; Classification algorithms; Euclidean distance; Humans; Kernel; Nearest neighbor searches; Principal component analysis; Spatial databases; Testing; Wavelet domain; Wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Communications Applications Conference, 2009. SIU 2009. IEEE 17th
Conference_Location :
Antalya
Print_ISBN :
978-1-4244-4435-9
Electronic_ISBN :
978-1-4244-4436-6
Type :
conf
DOI :
10.1109/SIU.2009.5136379
Filename :
5136379
Link To Document :
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