title: Statistical approach to shape from shading: reconstruction of three-dimensional face surfaces from single two-dimensional images.
abstract: The human visual system is proficient in perceiving three-dimensional shape from the shading patterns in a two-dimensional image. How it does this is not well understood and continues to be a question of fundamental and practical interest. In this paper we present a new quantitative approach to shape-from-shading that may provide some answers. We suggest that the brain, through evolution or prior experience, has discovered that objects can be classified into lower-dimensional object-classes as to their shape. Extraction of shape from shading is then equivalent to the much simpler problem of parameter estimation in a low-dimensional space. We carry out this proposal for an important class of three-dimensional (3D) objects: human heads. From an ensemble of several hundred laser-scanned 3D heads, we use principal component analysis to derive a low-dimensional parameterization of head shape space. An algorithm for solving shape-from-shading using this representation is presented. It works well even on real images where it is able to recover the 3D surface for a given person, maintaining facial detail and identity, from a single 2D image of his face. This algorithm has applications in face recognition and animation.
authors.affiliation: Computational Neuroscience Laboratory, Rockefeller University, Ney York, NY 10021-6399, USA.
authors.forename: J J
authors.initials: JJ
authors.lastname: Atick
authors.forename: P A
authors.initials: PA
authors.lastname: Griffin
authors.forename: A N
authors.initials: AN
authors.lastname: Redlich
mesh_terms: D001921:Brain / Q000502:physiology*; D005145:Face / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted