Personal recognition system using hand modality based on local features

Human hand is a physiological biometric trait employed in order to characterize and identify an individual. It is considered as one of the most popular biometric technologies especially in forensic applications, due to its high users acceptance compared to other biometric technologies. In this paper, we propose a new hand biometric system for personal identity verification, combining two local features at matching score level. Indeed, these features are represented by SIFT (Scale Invariant Feature Transform) descriptors and geometrical measurements of the hand. Our experiments are evaluated using Bogazici University Hand database containing 1926 left hand images acquired from 642 subjects and showed promising results which are comparable with other approaches.