On the recognition of occluded shapes and generic faces using multiple-template expansion matching

An approach for template matching by signal expansion into a set of nonorthogonal template-similar basis functions and its generalization to multiple templates is described. The single-template method is proven to be equivalent to restoration of undergraded images using the Wiener filter and optimizes a practically defined matching criterion called discriminative signal-to-noise ratio (DSNR). Compared to the matched filtering approach which optimizes the SNR, expansion matching is more robust in conditions of noise, superposition, and severe occlusion. The multiple-template expansion filter presented is generalized to match more than one template. It is also optimal in terms of DSNR. It can be designed to elicit any desired response for each of the templates