Improving hand-based verification through online finger template update based on fused confidences

Since the biometric data tends to have large intra-class variability, it is possible for the enrolled templates to be significantly different from acquired samples during system's operation. The majority of existing techniques in the literature, namely self update, update a template set by using a confidently verified input sample in order to avoid the introduction of impostors into the template set of a client. Therefore these techniques can only exploit the input sample very similar to the current template set leading to local optimization of a template set. To address this issue, this paper introduces a technique by decomposing the hand silhouette into the different parts (i.e. fingers) and analyzing the confidences of these parts in order to lead to global optimization of templates. In the proposed method, first the hand silhouette is divided in different parts corresponding to the fingers. Then the confidence of each finger, as well as its identity, is evaluated by a support vector data description (SVDD). The confidence of a query hand is determined by the maximum confidence of all fingers. If the maximum confidence is higher than a threshold, the boundaries of all fingers' SVDDs are incrementally updated to learn the variations of the input data. The motivation behind this technique is that the temporal changes that may occur in the fingers are uncorrelated in such a way that the confidence of each finger can be significantly different from the others. As a result those fingers with difficult intra-class variations (low confidence) can be used in the update process by this technique. The experimental results show the effectiveness of the proposed technique in comparison to the state of the art self-update technique especially at low false acceptance rates.