Conference Paper

A Linear Approach to Face Shape and Texture Recovery using a 3D Morphable Model.

DOI: 10.5244/C.24.75 Conference: British Machine Vision Conference, BMVC 2010, Aberystwyth, UK, August 31 - September 3, 2010. Proceedings
Source: DBLP
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Available from: William A. P. Smith, Oct 17, 2014
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    • "The key advantage of this approach is that shape and texture estimation can both be posed as multilinear (and hence convex) fitting problems and solved independently. We begin by estimating 3D shape parameters and the camera matrix by fitting to sparse feature points using the algorithm proposed in [1]. This approach uses an empirical model of the generalisation capability of each feature point. "
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    ABSTRACT: Well known results in inverse rendering show that recovery of unconstrained illumi-nation, texture and reflectance properties from a single image is ill-posed. On the other hand, in the domain of faces linear statistical models have been shown to efficiently characterise variations in face shape and texture. In this paper we show how the inverse rendering process can be constrained using a morphable model of face shape and tex-ture. Starting with a shape estimate recovered using the statistical shape model, we show that the image formation process leads to a system of equations which is multilinear in the unknowns. We are able to estimate diffuse texture, specular reflectance properties, the illumination environment and camera properties from a single image. Our approach uses relaxed assumptions and offers improved performance in comparison to the current state-of-the-art morphable model fitting algorithms.
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    ABSTRACT: 3D face shape provides a pose and illumination invariant description of human faces. In this paper, we propose a novel component based method to recover the full 3D face shape from a set of sparse feature points. We use a local linear fitting (LLF) scheme so that reconstruction of each subregion depends on both its own vertices and adjacent subregions. This method results in a separate set of shape coefficients each emphasizing the quality of one subregion and improves the model expressiveness. Experiments show that the LLF strategy significantly reduces the model residual error, and thus reduces the sparse reconstruction error under pose variations. Moreover, the problem of estimating pose parameters is revisited, and we use a joint optimization method to improve the reconstruction quality under unknown pose. We evaluate the sensitivity of our method to the selection of feature points. Simulation results show that our method is more robust than prevailing methods.
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    ABSTRACT: This paper proposes a model-based approach for 3D facial shape recovery using a small set of feature points from an input image of unknown pose and illumination. Previous model-based approaches usually require both texture (shading) and shape information from the input image in order to perform 3D facial shape recovery. However, the methods discussed here need only the 2D feature points from a single input image to reconstruct the 3D shape. Experimental results show acceptable reconstructed shapes when compared to the ground truth and previous approaches. This work has potential value in applications such face recognition at-a distance (FRAD), where the classical shape-from-X (e.g., stereo, motion and shading) algorithms are not feasible due to input image quality.
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