Browsing by Author "Beukes, Emile"

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    Hand vein-based biometric authentication using neural networks
    (Stellenbosch : Stellenbosch University, 2024-03) Beukes, Emile; Coetzer, Johannes; Stellenbosch University. Faculty of Science. Dept. of Applied Mathematics.
    ENGLISH ABSTRACT: The feasibility of employing convolutional neural networks for the purpose of authenticating an individual based on a near infra-red image of his/her dorsal hand vein pattern is inves- tigated in this study. The proficiency of different architectural designs associated with sim- ilarity measure networks (SMNs), in particular two-channel SMNs and Siamese SMNs, are compared. Four different combinations of neural network layers are investigated for each of the aforementioned SMNs. Three different levels of preprocessing are applied to the hand vein images in order to investigate the relevance of information surrounding the actual hand veins on the proficiency of the networks. The proficiency of the proposed systems is gauged within the context of two real-world scenarios, namely the individual dependent scenario (IDS) and the individual independent scenario (IIS). A tailor-made network is trained for each client during enrolment in mere minutes within the context of the IDS, while a single net- work is trained in a once-off fashion prior to the enrolment of any clients within the context of the IIS. Two publicly available hand vein databases namely the Bosphorus and Wilches databases are investigated within the context of this study. An artificially generated hand vein database, namely the GenVeins database, is developed in this study for the purpose of acquiring a set of training individuals that is large enough so as to be representative of the entire population. The motivation behind the creation of the GenVeins database constitutes the fact that experimental results indicate that system proficiency is severely impaired when training on an insufficient number of different individuals within the context of the IIS. The systems proposed in this study are therefore considered implementation-ready in the sense that they are either trained in a (1) tailor-made fashion for each client enrolled into the sys- tem in real time or in a (2) once-off fashion on a set of fictitious individuals that is sufficiently representative of the entire population. The proposed systems do therefore not merely serve as so-called proofs-of-concept (POCs) in which a system is trained and tested on the same set of individuals. These POCs are clearly not feasible within the context of any real world scenario.
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    Hand vein-based biometric authentication with limited training samples
    (Stellenbosch : Stellenbosch University, 2018-03) Beukes, Emile; Coetzer, Johannes; Swanepoel, J.; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences (Applied Mathematics)
    ENGLISH ABSTRACT : A number of novel hand vein-based biometric authentication systems are proposed. Said systems are non-intrusive and may for example assist with user authentication at automated teller machines. An infrared image of either the dorsal or ventral surface of an individual's hand is acquired through specialised equipment, after which the geometrical properties of the hand are used to extract a suitable region of interest (ROI). A novel protocol, which is based on morphological reconstruction, is employed for the purpose of isolating the veins within the ROI. Feature vectors are extracted from the isolated veins through the calculation of the discrete Radon transform. The feature vectors are appropriately normalised in order to ensure rotational, translational and scale invariance. The dissimilarity between the corresponding feature vectors extracted from a questioned image and a reference image belonging to the claimed client are represented by an average Euclidean or dynamic time warping-based distance. A score-based or rank-based classi er is subsequently employed for authentication purposes. It is demonstrated that, when only one training sample (of arbitrary quality) is available per client, and the client is granted six opportunities for authentication, an average error rate (AER) of 2.85% is achievable for a data set that contains dorsal hand vein patterns from 100 individuals. In a scenario where the single training sample is guaranteed to be of very high quality and the client is granted only three opportunities for authentication, the AER may be reduced to 0.77%.