In this paper we propose a technique for both finding vein regions from thermal dorsal hand images and extracting features for biometric recognition; our technique analyzes the geometry of the hand to isolate the vein regions and extract some features (vein bifurcations and ending points) for being used as features in the training sets for classifiers. Commonly, the features extracted are used as geometric and descriptive representation of the vein patterns which are matched with hand vein images in a database in order to determine/verify the person's identity.
In Biometric recognition, commonly the information about the biometric to be analyzed is contained in digital images, in particular this work is focused on analyzing hand dorsal veins as biometric. A basic process during the recognition stage is the feature (minutiae) extraction; when images are captured from several people it is very difficult to obtain a global alignment and as consequence different orientation images are obtained which makes more difficult the feature extraction. In this work we propose an approach based on central hand points for auto-rotating hand thermal images; based on the results our approach is able to rotate images from different orientations and obtains homogeneous alignment of the tested images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.