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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    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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    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: The 3D Morphable Model (3DMM) and the Structure from Motion (SfM) methods are widely used for 3D facial reconstruction from 2D single-view or multiple-view images. However, model-based methods suffer from disadvantages such as high computational costs and vulnerability to local minima and head pose variations. The SfM-based methods require multiple facial images in various poses. To overcome these disadvantages, we propose a single-view-based 3D facial reconstruction method that is person-specific and robust to pose variations. Our proposed method combines the simplified 3DMM and the SfM methods. First, 2D initial frontal Facial Feature Points (FFPs) are estimated from a preliminary 3D facial image that is reconstructed by the simplified 3DMM. Second, a bilateral symmetric facial image and its corresponding FFPs are obtained from the original side-view image and corresponding FFPs by using the mirroring technique. Finally, a more accurate the 3D facial shape is reconstructed by the SfM using the frontal, original, and bilateral symmetric FFPs. We evaluated the proposed method using facial images in 35 different poses. The reconstructed facial images and the ground-truth 3D facial shapes obtained from the scanner were compared. The proposed method proved more robust to pose variations than 3DMM. The average 3D Root Mean Square Error (RMSE) between the reconstructed and ground-truth 3D faces was less than 2.6 mm when 2D FFPs were manually annotated, and less than 3.5 mm when automatically annotated.
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