Palmprint Recognition by Applying Wavelet-Based Kernel PCA

作     者：Murat Ekinci Murat Aykut 

作者机构：Computer Vision Lab Department of Computer Engineering Karadeniz Technical University 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2008年第23卷第5期

页      面：851-861页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported fully by the TUBITAK Research Project under Grant No. 107E212 

主　　题：palmprint recognition kernel PCA wavelet transform biometrics pattern recognition 

摘      要：This paper presents a wavelet-based kernel Principal Component Analysis （PCA） method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coefficients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coefficients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classifier. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.