A Novel Biometric System Based Hand Vein Recognition

Abstract

Abstract As a reliable and universal biometric characteristic, hand vein identification has attracted many interested researchers. The hand vein identification system exhibits several excellent advantages in the biometric domain because it meets the increasing demand of accuracy and robustness. In this paper, we propose a new biometric recognition system based on hand vein features. The detection and extraction of the region of interest is based on Voronoi Decomposition. Furthermore, contrast enhancement is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Starting with the basic Gaussian Matched Filter (GMF) and its variant, we propose a new technique called the Improved Gaussian Matched Filter method (IMPGMF) surmounting the false detection of hand vessels with the traditional GMF. Feature points are then detected based on ending and bifurcation structures in the image map obtained with the proposed IMPGMF method and taken as signature for our biometric system. Then, Artificial Neural Networks (ANN) are used for the classification step. In the validation step, we used a 1500 hand vein image from the BOSPHORUS database. The Error Equal Rate is 0.01 % and the Area Under curve of the corresponding system is approximately 0.98, demonstrating a very high security level.