The importance of not being mean: DFM—a norm-referenced data model for face pattern recognition

A successful, mature system for face recognition, Elastic Bunch Graph Matching, represents a human face as a graph in which nodes are labeled with double precision floating-point vectors called “jets”. Each jet in a model graph comprises the responses at one fiducial point, or face landmark, of a convolution of the image with a set of self-similar Gabor wavelets of various orientations and spatial scales. Gabor wavelets are scientifically reasonable models for the receptive field profiles of simple cells in early visual cortex. Heretofore, the recognition process simply searched for the stored model graph with the greatest total jet-similarity to a presented image graph. The most widely used measure of jet similarity is the sum over the graph of the dot-products of jets normalized to unit length. We improve significantly upon this system, with orders of magnitude improvements in time and space complexity and marked reductions in recognition error rates. We accomplish these improvements by recasting the concatenated vector of model-graph jets as a binary string, or b-string, comprising bits with one-to-one correspondence to the floating-point coefficients in the model graph. The b-string roughly models a pattern of correlated firing among a population of idealized neurons. The “on” bits of the b-string correspond to the identities of the coefficients that deviate the greatest amount from the corresponding mean coefficient values. We show that this simple recoding consistently reduces recognition error rates by margins exceeding thirty percent. Our investigations support the hypothesis that the b-string representation for faces is extremely efficient and, ultimately, information preserving