DAS, Harvard University
(617)495-9526
yuille@hrl.harvard.edu
FAX (617) 496-6404
Note: address will be changed after January 1 to:
Smith-Kettlewell Eye Research Institute,
2232 Webster Street,
San Francisco, CA 94115.
(415) 561-1620 (Smith-Kettlewell)
yuille@skivs.ski.org
We have pursued two approaches to this problem. The first is based on two-dimensional face templates. The second attempts to learn and recognize three-dimensional models.
The two-dimensional face template work is summarized in the PhD thesis of Peter Hallinan. It consists of using a linear model for lighting variation (P.W. Hallinan. ``A low-dimensional representation of human faces for arbitrary lighting conditions''. In Proc. CVPR. 1994) to model changes in illumination. In addition it uses two-dimensional spatial warps of the images to model differences between different faces and small changes in viewing direction or expression. The resulting system (P.W. Hallinan. PhD. DAS, Harvard. 1995), subject to these restrictions, is able to recognize faces under extreme changes of lighting conditions, to distinguish between faces and non-faces, and to detect faces in an image.
This work has been extended by: (i) showing that the linear lighting model works for a range of objects (R. Epstein, P.W. Hallinan, and A.L. Yuille ``Five plus or minus two eigenimages suffic''. In Proc. IEEE Workshop on Physics-Based Modelling. 1995.), (ii) understanding under what assumptions the two-dimensional spatial image warps correspond to three-dimensional shape changes. (A.L. Yuille, M. Ferraro, and T. Zhang. ``Shape from Warping''. In preparation. 1995).
More recently, we have attempted to improve on this work by using three-dimensional object models. These models are learnt from the data assuming only a set of images of the object taken under identical viewpoint but different lighting conditions. We have shown that Lambertian lighting models are sufficiently accurate for the face provided they are applied robustly so that cast shadows and specularities can be treated as residuals. If prior knowledge of faces is used then these three-dimensional models may be learnt from a single image. Once these three-dimensional models are learnt then recognition can proceed by finding the specific face and lighting conditions that best synthesize the image. (P.N. Belhumeur, A.L. Yuille, and R. Epstein. ``Learning and Recognizing Objects using Illumination Subspaces.'' In preparation. 1995).
P.W. Hallinan. PhD. DAS, Harvard. 1995.
R. Epstein, P.W. Hallinan, and A.L. Yuille ``Five plus or minus two eigenimages suffice''. In Proc. IEEE Workshop on Physics-Based Modelling. 1995.
A.L. Yuille, M. Ferraro, and T. Zhang. ``Shape from Warping''. In preparation. 1995
P.N. Belhumeur, A.L. Yuille, and R. Epstein. ``Learning and Recognizing Objects using Illumination Subspaces.'' In preparation. 1995.
Active Vision. Eds. A. Blake and A.L. Yuille. MIT Press, Cambridge, MA. 1992.
Adaptive Human Interfaces.
Intelligent Interactive Systems for Persons with Disabilities.