Abstract
This paper presents a robust dorsal hand vein authentication system. A new method is proposed for the region of interest extraction using fingertips and finger valley key points. Some new features and a new classifier are proposed based on information set theory. Information set stems from a fuzzy set on representing the uncertainty in its attribute/information source values using the information-theoretic entropy function. The new feature types include vein effective information, vein energy feature, vein sigmoid feature, Shannon transform feature, and composite transform feature. A classifier called the improved Hanman classifier is formulated from training and test feature vectors using Frank t-norm and the entropy function. The performance and robustness are evaluated on GPDS and BOSPHORUS palm dorsal vein database under both the constrained and unconstrained conditions.
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