Directional energy based palmprint identification using non subsampled contourlet transform

Authors:
Mustafa Mumtaz;Atif Bin Mansoor;Hassan Masood
Affiliations:
National University of Sciences and Technology, Rawalpindi, Pakistan;National University of Sciences and Technology, Rawalpindi, Pakistan;National University of Sciences and Technology, Rawalpindi, Pakistan
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 5
Cited 2

Biometric Identification through Hand Geometry Measurements

IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Palmprint Identification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of palmprints by wavelet signatures via directional context modeling

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Palm line extraction and matching for personal authentication

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Personal recognition using hand shape and texture

IEEE Transactions on Image Processing

An evaluation of wavelet kernels for palmprint based recognition

AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
A feature level multimodal approach for palmprint identification using directional subband energies

Journal of Network and Computer Applications

Quantified Score

Hi-index	0.00

Visualization

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 novel palmprint based identification approach which uses the textural information available on the palmprint by employing the Non Subsampled Contourlet Transform (NSCT). After establishing the region of interest (ROI), the two dimensional (2-D) spectrum is divided into fine slices, using iterated directional filterbanks. Next, directional energy component for each block from the decomposed subband outputs is computed. The proposed algorithm captures both local and global details in a palmprint as a compact fixed length palm code. Palmprint matching is then performed using Normalized Euclidean Distance classifier. The algorithm is tested on a total of 7752 palm images, acquired from the standard database of Polytechnic University of Hong Kong. The experimental results demonstrated the feasibility of the proposed system by exhibiting Decidability Index of 2.8125 and Equal Error Rate of 0.1604%, better than the reported techniques in literature.