Natural Basis Functions and Topographic Memory for Face Recognition

Recent work regarding the statistics of natural images has revealed that the dominant eigenvectors of arbitrary natural images closely approximate various oriented derivative-of-Gaussian 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 expression-invariant 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. 1 Introduction The problem of object recognition has been a central subject in the field of computer vision. An especially interesting albeit difficult subproblem is that of recognizing human faces. In addition to the difficulties posed by changing viewing conditions, computational methods for face recognition have had to confront the fact that faces are complex non-rigid stimuli that defy easy geometric characterizations and form a dense cluster in the multidimensional space of input images. One of the most important issues in face recognition has therefore been the representation of faces. Early schemes for face recognition utilized geometrical representations; prominent features such as eyes, nose, mouth, and chin were detected and geometrical models of faces given by feature vectors whose dimensions, for instance, denoted the relative positions of the facial features were used for the purposes of recognition [Bledsoe, 1966; Kanade, 1973]. Recently, researchers have reported successful results using photometric representations i.e. representations that are computed directly from the intensity values of the input image. Some prominent examples include face representations based on biologically-motivated Gabor filter "jets" [Buhmann et al., 1990], randomly placed zeroth-order Gaussian kernels [Edelman et a/. This paper explores the use of an iconic representation of human faces that exploits the dimensionality-reducing properties of PCA. However, unlike previous approaches employing …