Relating intensities with three-dimensional facial shape using partial least squares

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Relating intensities with three-dimensional facial shape using partial least squares

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The authors apply partial least squares regression to predict three-dimensional (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 independent variables that are associated with large variations in the space of dependent variables. We use this idea to construct statistical models of intensity and 3D shape that capture strongly linked variations in both spaces. This decomposition leads to the construction of two different models that capture common 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, improving results obtained through canonical correlation analysis.

Inspec keywords: shape recognition; correlation methods; regression analysis; least squares approximations

Other keywords: canonical correlation analysis; partial least squares regression; intensity images variables; 3D facial shape

Subjects: Other topics in statistics; Interpolation and function approximation (numerical analysis); Image recognition; Interpolation and function approximation (numerical analysis); Other topics in statistics; Computer vision and image processing techniques

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