Vein recognition based on minutiae features in the dorsal venous network of the hand

This paper presents a novel, local feature-based vein representation method based on minutiae features from skeleton images of venous networks. The main motivation is to learn the most discriminative regions and features of dorsal hand veins to identify persons who are scanned. These minutiae features include end points and the arc lines between the two end points as measured along the boundary of the region of interest. In addition, we propose a dynamic pattern tree to accelerate matching performance and evaluate the discriminatory power of these feature points for verifying a person’s identity. In a comparison with six existing verification algorithms, the proposed method achieved the highest accuracy in the lowest tested matching time.

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