Abstract
This paper presents a hand biometric system for contact-less, platform-free scenarios, proposing innovative methods in feature extraction, template creation and template matching. The evaluation of the proposed method considers both the use of three contact-less publicly available hand databases, and the comparison of the performance to two competitive pattern recognition techniques existing in literature: namely Support Vector Machines (SVM) and k-Nearest Neighbour (k-NN). Results highlight the fact that the proposed method outcomes existing approaches in literature in terms of computational cost, accuracy in human identification, number of extracted features and number of samples for template creation. The proposed method is a suitable solution for human identification in contact-less scenarios based on hand biometrics, providing a feasible solution to devices with limited hardware requirements like mobile devices.
Highlights
At present, trends in biometrics are inclined to provided human identification and verification without requiring any contact with acquisition devices
Hand biometrics is evolving to contact-less, platform-free scenarios where hand images are acquired in free air, increasing the user acceptability and usability
This paper has presented a biometric system based on hand geometry oriented to contact-less and platform-free scenarios
Summary
Trends in biometrics are inclined to provided human identification and verification without requiring any contact with acquisition devices. Hand biometrics is evolving to contact-less, platform-free scenarios where hand images are acquired in free air, increasing the user acceptability and usability. This fact provokes an additional effort in segmentation, feature extraction, template creation and template matching, since these scenarios imply more variation in terms of distance to camera, hand rotation, hand pose and unconstrained environmental conditions. The main contribution of this paper is threefold: firstly, a feature extraction method is proposed, providing invariant hand measurements to previous changes; second contribution consists of providing a template creation based on hand geometric distances, requiring information from only one individual, without considering data from the rest of individuals within the database; a proposal for template matching is proposed, minimizing the intra-class similarity and maximizing the inter-class likeliness.
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