Wavelet Model-based Stereo for Fast, Robust Face Reconstruction

Abstract

When reconstructing a specific type or class of object using stereo, we can leverage prior knowledge of the shape of that type of object. A popular class of object to reconstruct is the human face. In this paper we learn a statistical wavelet prior of the shape of the human face and use it to constrain stereo reconstruction within a Bayesian framework. We initialize our algorithm with a, typically noisy, point cloud from a standard stereo algorithm, and search our parameter space for the shape that best fits the point cloud. Due to the wavelet basis, our shape parameters can be optimized independently, thus simplifying and accelerating the search. We follow this by optimizing for a secondary prior and observation: smoothing and photo consistency. Our method is fast, and is robust to noise and outliers. Additionally, we obtain a shape in an parameterized and corresponded shape space, making it ready for further processing such as tracking, recognition or statistical analysis.