Mosaicfaces: a discrete representation for face recognition

Most face recognition algorithms use a "distance- based" approach: gallery and probe images are projected into a low dimensional feature space and decisions about matching are based on distance in this space. In this paper we use a very different representation, where each face is approximated by a regular grid of patches (a mosaicface). Each of these patches is chosen from a library. Faces are now represented as a list of indices to this library. Since there is no obvious way to measure distance between two such lists, we use a probabilistic approach in which the observed face data is explained by a generative model. There are two phases: (i) Learning - we estimate library contents and associated variability (noise), (ii) Recognition - we evaluate the probability that probe and gallery images were generated from the same library patches. Our method performs significantly better than contemporary approaches in the presence of large illumination changes. Variation in viewing conditions is handled by extending this model to learn equivalences between multiple patch appearances. We demonstrate that our method provides a major improvement on the lighting subset of the XM2VTS database compared to "distance-based" methods.