Natural Basis Functions and Topographic Memory for Face Recognition

Abstract

Recent work regarding the statistics of natural images has revealed that the dominant eigenvectors of arbitrary natural images closely approximate various oriented derivative-ofGaussian functions; these functions have also been shown to provide the best fit to the receptive field profiles of cells in the primate striate cortex. We propose a scheme for expressioninvariant face recognition that employs a fixed set of these "natural" basis functions to generate multiscale iconic representations of human faces . Using a fixed set of basis functions obviates the need for recomputing eigenvectors (a step that was necessary in some previous approaches employing principal component analysis (PCA) for recognition) while at the same time retaining the redundancy-reducing properties of PCA . A face is represented by a set of iconic representations automatically extracted from an input image. The description thus obtained is stored in a topographically-organized sparse distributed memory that is based on a model of human long-term memory first proposed by Kanerva. We describe experimental results for an implementation of the method on a pipeline image processor that is capable of achieving near real-time recognition by exploiting the processor's frame-rate convolution capability for indexing purposes.