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… Click to show full 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.
               
Click one of the above tabs to view related content.