Personal recognition system using hand modality based on local features

Abstract

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.

Hand-Based Biometric Recognition Technique – Survey

“…The scanner can also be a biometric sensor [42,49,54]. The sensor captures either a 2D [55][56][57] or a 3D [48,58,59] image of the hand. 3D images are obtained using two cameras, with the help of mirrors, or they can be obtained using a 3D digitizer [60].…”

“…Mathematical morphology is a mathematical tool that can solve this problem and connect disconnected fingers. Mathematical morphology is also used to remove rings, bracelets or watches [56,76,77].…”

“…In this phase, a set of biometric features obtained from a recognized person's biometric sample is compared with a reference template or templates (stored in a database). The score is then generated using the following methods: When recognizing a person based on hand geometry and hand shape, various metrics are used to determine the distance, such as the Euclidean metric [37,56,70], the Hamming metric [39], or the Chebyshev metric [80].…”

Dense Hand-CNN: A Novel CNN Architecture based on Later Fusion of Neural and Wavelet Features for Identity Recognition

“…Reference [36] applied the sparse representation of SIFT to implement a touchless method for palmprint identification by extracting the left and right palms' print features. The SVM probability distribution detector was used to produce the rank level fusion in finalizing a personal identification.…”

“…studies yielded competitive palmprint identification findings on the bases of REgim Sfax Tunisia (REST) hand database [36] and CASIA Palmprint Database [1]. In particular, they developed a bimodal identification approach using SIFT descriptors for obtaining hand shape and palmprint features.…”