Talks

Pattern detection problems require a separation between two classes, ‘Target’ and ‘Clutter’, where the probability of the former is substantially smaller than that of the latter. We describe a new classifier that exploits this property, yielding a low complexity yet effective target detection algorithm. This Maximal Rejection Classifier (MRC) algorithm is based on successive rejection operations. Each rejection stage is performed using a linear projection followed by thresholding. The projection direction is designed to maximize the number of ‘Clutter’ points rejected from further consideration. An application of detecting frontal and vertical faces in images is demonstrated using the MRC, with encouraging results.