In this paper we propose a new classifier -the Maximal Rejection Classifier (MRC) -for target detection. Unlike pattern recognition, pattern detection problems require a separation between two classes, Target and Clutter, where the probability of the former is substantially smaller, compared to that of the latter. The MRC is a linear classifier, based on successive rejection operations. Each rejection is performed using a projection followed by thresholding. In contrast to common classifiers the projection vector is influenced by the probabilities of obtaining target or clutter signals. The projection vector is designed to minimize the expected number of operations until detection. In the case where the probabilities of target and clutter signals are equal, it is shown that the Fisher linear discriminant is optimal in the above sense. However, in more common cases where the probablitities are quite different, a new optimal classifier is suggested. An application of detecting frontal faces in images is demonstrated using the MRC with encouraging results.
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