Receptive field spaces and class-based generalization from a single view in face recognition

Abstract

We describe a computational model of face recognition, which generalizes from single views of faces by taking advantage of prior experience with other faces, seen under a wider range of viewing conditions. The model represents face images by vectors of activities of graded overlapping receptive fields (RFS). It relies on high-spatial-frequency information to estimate the viewing conditions, which are then used to normalize (via a transformation specific for faces), and identify the low-spatial-frequency representation of the input. The class-specific transformation approach allows the model to replicate a series of psychophysical findings on face recognition and constitutes an advance over current face-recognition methods, which are incapable of generalization from a single example.