Shape from recognition and learning: recovery of 3-D face shapes
Nandy, D.
Ben-Arie, J.
Dept. of Electr. Eng. & Comput. Sci., Illinois Univ., Chicago, IL;
This paper appears in: Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
Publication Date: 1999
Volume: 2,
On page(s): -7 Vol. 2
Meeting Date: 06/23/1999 - 06/25/1999
Location: Fort Collins, CO, USA
ISBN: 0-7695-0149-4
References Cited: 16
INSPEC Accession Number: 6338756
Digital Object Identifier: 10.1109/CVPR.1999.784600
Current Version Published: 2002-08-06
Abstract
In this paper a novel framework for the recovery of 3D surfaces of
faces from single images is developed. The underlying principle is shape
from recognition, i.e. the idea that pre-recognizing face parts can
constrain the space of possible solutions to the image irradiance
equation, thus allowing robust recovery of the 3D structure of a
specific part. Shape recovery of the recognized part is based on
specialized backpropagation based neural networks, each of which is
employed in the recovery of a particular face part. Representation using
principal components allows to efficiently encode classes of objects
such as nose, lips, etc. The specialized networks are designed and
trained to map the principal component coefficients of the shading
images to another set of principal component coefficients that represent
the corresponding 3D surface shapes. A method for integrating recovered
3D surface regions by minimizing the sum squared error in overlapping
areas is also derived. Quantitative analysis of the reconstruction of
the surface parts show relatively small errors indicating that the
method is robust and accurate. The recovery of a complete face is
performed by minimal squared error merging efface parts
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
