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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
3D face shape approximation from intensities using Partial Least Squares
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Mario Castelan, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Carr. Saltilo - Monterrey, Ramos Arizpe, Coah., México
Johan Van Horebeek, Centro de Investigación en Matemáticas, Jalisco S/N, Valenciana 36240, Guanajuato, Gto., México
In this paper, we apply Partial Least Squares (PLS) regression to predict 3D face shape from a single image. PLS describes the relationship between independent (intensity images) and dependent (3D shape) variables by seeking directions in the space of the independent variables that are associated with high variations in the dependent variables. We exploit this idea to construct statistical models of intensity and 3D shape that express strongly linked variations in both spaces. The outcome of this decomposition is the construction of two different models which express coupled variations in 3D shape and intensity. Using the intensity model, a set of parameters is obtained from out-of-training intensity examples. These intensity parameters can then be used directly in the 3D shape model to approximate facial shape. Experiments show that prediction is achieved with reasonable accuracy.
Citation:
Mario Castelan, Johan Van Horebeek, "3D face shape approximation from intensities using Partial Least Squares," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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