High Performance Palmprint Identification System Based On Two Dimensional Gabor

Abstract

The palmprint is a new recognition method in physiological biometrics. In palmprint region of interest (ROI), the segmentation and feature extraction are two important issues. The main problem in palmprint recognition system is how to extract the region of interest (ROI) and the features of palmprint. This paper introduces two steps in center of mass moment and the application of method for ROI segmentation and then to apply the Gabor two dimensional (2D) filters to obtain palm code as palmprint feature vector. Normalized Hamming distance is used to measure the similarity degrees of two feature vectors of palmprint. The system has been tested by using database 1000 palmprint images which was generated from 5 groups of samples from 200 persons selected randomly. Experiment results show that this system can achieve a high performance with success rate about 98.7% (FRR=1.1667%, FAR=0.1111%, T=0.376).