Richard Youmaran

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This paper addresses the issue of the information content of a biometric image or system. We define biometric information as the decrease in uncertainty about the identity of a person due to a set of biometric measurements. We then show that the biometric information for a person may be calculated by the relative entropy D(pparq) between the population(More)
This paper develops an approach to measure the information content of a biometric feature representation. We define biometric information as the decrease in uncertainty about the identity of a person due to a set of biometric measurements. We then show that the biometric feature information for a person may be calculated by the relative entropy(More)
We ask: how many bits of information (in the Shannon sense) do we get from a set of EIT measurements? Here, the term information in measurements (IM) is defined as: the decrease in uncertainty about the contents of a medium, due to a set of measurements. This decrease in uncertainty is quantified by the change from the inter-class model, q, defined by the(More)
Current background subtraction methods require background modeling to handle dynamic backgrounds. The purpose of our study is to investigate a background template subtraction method to detect foreground objects in the presence of background variations. The method uses a single reference image but the change detection process allows change in the background(More)
In the current context of heightened concerns with explosives security, there is significant interest in technological controls to improve security. It is important to be able to control what is fired, by whom, where and when. This paper describes research Orica has performed to investigate and test biometric systems to address the question of "by whom".(More)
Regularization is an important tool for restoration of images from noisy and blurred data. In this paper, we present a novel regularization technique (CGTik) that augments the conjugate gradient least-square (CGLS) algorithm with Tikhonov-like prior information term. This technique requires the appropriate selection of two hyper-parameters, the number of(More)