Personal identification using palmprint and contourlet transform

Abstract

Palmprint based personal verification has gained preference over other biometric modalities due to its ease of acquisition, high user acceptance and reliability. This paper presents a new palmprint based identification approach which uses the textural information available on the palmprint by employing the Contourlet Transform (CT). Center of the palm is calculated using the Distance Transform and by calculating the parameters for the best fitting ellipse, the alignment of hand 'θ' is found. Rotational invariance is achieved by cropping a square region of size 256 x 256 pixels around the center aligned at θ degrees. After establishing the region of interest (ROI), the two dimensional (2-D) spectrums is divided into fine slices, using iterated directional filterbanks. Next, directional energy components for each block of the decomposed subband outputs are computed. The proposed algorithm captures both local and global details in a palmprint as a compact fixed length palm code of the computed directional energies. Palmprint matching is then performed using Normalized Euclidean Distance classifier. The proposed algorithm is tested on a total of 500 palm images of GPDS Hand database, acquired from University of Las Palmas de Gran Canaria. The experimental results demonstrated the feasibility of the proposed system by exhibiting Genuine Acceptance Rate of 98.2%, Decidability Index of 2.6212 and Equal Error Rate of 0.7082%. © 2008 Springer Berlin Heidelberg.