marching cubes algorithm is obviously colorless. Our task is to add the color texture of the same patient actually taken with a digital camera to the colorless 3D surface. To do this it needs an accurate registration between the 3D facial image and the color photograph. Our approach is to set up a virtual camera around the 3D facial surface to register the virtual camera images with the corresponding color photographs by automatically adjusting seven parameters of the virtual camera. The camera parameters consists of three rotations, three translations and one scale factor. The registration algorithm has been developed based upon Besl and McKay's iterative closest point (ICP) algorithm." />


Automatic Reconstruction of 3D Human Face from CT and Color Photographs

Ali Md. HAIDER  Toyohisa KANEKO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E82-D   No.9   pp.1287-1293
Publication Date: 1999/09/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Processing,Computer Graphics and Pattern Recognition
Keyword: 
3D face,  image registration,  medical visualization,  color mapping,  

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Summary: 
This paper proposes an automatic method for reconstructing a realistic 3D facial image from CT (computer tomography) and three color photographs: front, left and right views, which can be linked easily with the underlying bone and soft tissue models. This work is the first part of our final goal, "the prediction of patient's facial appearance after cancer surgery" such as removal of a part of bone or soft tissues. The 3D facial surface derived from CT by the marching cubes algorithm is obviously colorless. Our task is to add the color texture of the same patient actually taken with a digital camera to the colorless 3D surface. To do this it needs an accurate registration between the 3D facial image and the color photograph. Our approach is to set up a virtual camera around the 3D facial surface to register the virtual camera images with the corresponding color photographs by automatically adjusting seven parameters of the virtual camera. The camera parameters consists of three rotations, three translations and one scale factor. The registration algorithm has been developed based upon Besl and McKay's iterative closest point (ICP) algorithm.