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Over the past several years, the subcutaneous blood vessels have emerged as a new solution for identity management. Biometric systems based on hand veins are considered to be very promising for high security environments. In this paper, we propose a novel user authentication approach based on dorsal hand vein pattern analysis and multi-layer perceptron neural network classification. For image processing two different techniques are employed: rotation invariant Hough transform and clustering based segmentation and mathematic morphology. Both approaches lead to binary images containing the vein patterns. The vessel structure corresponding to hand image samples of the same user are used to extract the final features, independent of hand rotation and distance to the camera lens during acquisition. These characteristics are used to train the neural network, whereas are computed for the new input images to be classified as corresponding to one of the legitimate subjects or not. The experimental analysis shows that user classification with an equal error rate of 0.83% can be attained, bringing the advantages of proposed image processing techniques for vein detection and neural network classification into complete synergy.

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