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.

• Type: Journal
• Year: 2006
• Where: IVC
• Authors: Ajit Rajwade, Martin D. Levine
• Keywords: Apparent 3D Shape, IVC 2006, Minor Occlusions, Support Vector Regression