Kernel Principal Component Analysis of Gabor Features for Palmprint Recognition

This paper presents Gabor-based kernel Principal Component Analysis (KPCA) method by integrating the Gabor wavelet and the KPCA methods for palmprint recognition. The intensity values of the palmprint images extracted by using an image preprocessing method are first normalized. Then Gabor wavelets are applied to derive desirable palmprint features. The transformed palm images exhibit strong characteristics of spatial locality. scale, and orientation selectivity. The KPCA method nonlinearly maps the Gabor wavelet image into a high-dimensional feature space and the matching is realized by weighted Euclidean distance. The proposed algorithm has been successfully tested on the PolyU palmprint database which the samples were collected in two different sessions. Experimental results show that this method achieves 97.22% accuracy for PolyU dataset using 3850 images from 385 different palms captured in the first session as train set and the second session im0061 ges as test set.