A Scheme of Template Selection and Updating for Palmprint Authentication Systems

Abstract

The representativeness of templates is a fundamental problem in palmprint authentication systems, where false rejection rate rises if the enrolled templates are less representative of intra-class variations such as posture changes, lighting conditions, and scars. In order to solve this problem, existing techniques typically store multiple templates for the sake of temporary variations such as posture changes, and followed by templates updating to manage the periodical and permanent variations. However, the techniques in the literature do not combine template selection and updating organically to make the system maintain or even improve the representativeness of templates. In this paper, we propose a scheme to provide effective solutions to two important issues of palmprint authentication system: how to automatically select representative templates in a number of candidate samples, and how to update the templates if they are insufficient or are no longer representative of intra-class variations. In enrollment stage, the proposed scheme performs Chameleon clustering and selects the most representative template from each cluster. During authentication stage, the proposed scheme updates the templates in online mode based on historical hit-counts and incoming samples. The experimental results on our palmprint database have demonstrated the effectiveness of our templates selection and updating scheme.