Abstract: We present a system for recognizing human faces from single images out of a large database with one image per person. The task is difficult because of image variance in terms of position, size, expression and pose. The system collapses most of this variance by extracting concise face descriptions in the form of image graphs. In these, fiducial points on the face (eyes, mouth etc.) are described by sets of wavelet components (jets). Image graph extraction is based on a novel approach, the bunch graph, which is constructed from a small set of sample image graphs. Recognition is based on a straight-forward comparison of image graphs. We report here on recognition experiments with galleries of 250 images from the FERET database, also across different poses.
Keywords: face recognition, different poses, Gabor wavelets, elastic graph matching, bunch graph, ARPA/ARL FERET database