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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 D(pq)(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 substraction 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(More)
This paper presents a method to improve face detection by locating eyes in an image using infrared (IR) light. IR light produces Red-Eye effect making the pupil to shine more than in normal lighting conditions. The location of the eyes and the face contour are computed from the IR images using a collection of image processing techniques. The algorithm(More)
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