A Dorsal Vein Recognition System Based on Score Fusion Technique Combining Local and Statistical Features
The numerous problems of the traditional security technologies have led to a rise in developing biometric systems. Many traditional biometric systems, such as fingerprint, face, and iris systems have been studied extensively in the previous decades .Nowadays the concept of using the dorsal vein pattern in authentication that uses the vast network blood vessels underneath a person's skin showed to be a proven biometric technique. Different trails have been done to extract veins patterns and build systems that can give good performance. In this paper, we proposed an automatic system for human identification based on their dorsal vein patterns. Within this approach preprocessing steps are implemented to get the enhanced grayscale images with visible and clear veins. Two features extraction techniques are used, Principle Component Analysis (PCA) and Scale Invariant Feature Transform (SIFT) to get eigenveins and keypoints respectively. Statistical distance classifiers are implemented for classification. Finally the results from both classifiers are fused using simple min rule. The system has been successfully tested on a database of 100 users using MATLAB version 2009. The obtained results show that the proposed system outperforms the results of each individual system (PCA, SIFT)