A new  method is proposed for the region of interest (ROI) 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 representing the uncertainty in its attribute / information source values using the information-theoretic entropy function. The new feature types include vein effective information (VEI), vein energy feature (VEF), vein sigmoid feature (VSF), Shannon transform feature(STF) and composite transform Feature (CTF). A classifier called the improved Han man classifier (IHC) 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.

• Fingertip and Finger Valley ROI Extraction
• Improved Han Man Classifier (IHC)
• Information Set Theory Features