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2006

Facial pose from 3D data

11 years 9 months ago
Facial pose from 3D data
The distribution of the apparent 3D shape of human faces across the view-sphere is complex, owing to factors such as variations in identity, facial expression, minor occlusions and noise. In this paper, we use the technique of Support Vector Regression to learn a model relating facial shape (obtained from 3D scanners) to 3D pose in an identity-invariant manner. The proposed method yields an estimation accuracy of 97% to 99% within an error of +/- 9 degrees on a large set of data obtained from two different sources. The method could be used for pose estimation in a view-invariant face recognition system.
Ajit Rajwade, Martin D. Levine
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where IVC
Authors Ajit Rajwade, Martin D. Levine
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