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Palmprint recognition using kernel PCA of Gabor features

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2 Author(s)
Murat Ekinci ; Computer Vision and Pattern Recognition Lab., Department of Computer Engineering, Karadeniz Technical University, 62080 Trabzon, Turkey ; Murat Aykut

This paper presents a new method for automatic palmprint recognition based on kernel PCA method by integrating the Gabor wavelet representation of palm images. Gabor wavelets are first applied to derive desirable palmprint features. The Gabor transformed palm images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. These images can produce salient features that are most suitable for palmprint recognition. The kernel PCA method then nonlinearly maps the Gabor-wavelet image into a high-dimensional feature space. The proposed algorithm has been successfully tested on two different public data sets from the PolyU palmprint databases for which the samples were collected in two different sessions.

Published in:

Computer and Information Sciences, 2008. ISCIS '08. 23rd International Symposium on

Date of Conference:

27-29 Oct. 2008