Facial Image Analysis using Directional Statistics and Shape-from Shading

author: Edwin Hancock, Department of Computer Science, University of York
published: Sept. 20, 2010,   recorded: September 2010,   views: 3783


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Although the recovery of facial shape using shape-from-shading is an appealing idea, it is frustrated by problems such as concave-convex ambiguities, variable albedo, self shadowing and non-Lambertian reflectance. As such the devil resides in the detail. In this talk I will show how these problems can be overcome by incorporating a statistical model for surface normal direction within the shape-from-shading process. The main contribution of the talk is to develop a representation of the distribution of surface normals using the equidistant azimuthal projection from cartography, which transforms a distribution of surface normal direction on a unit sphere to a distribution of points on a tangent plane. I will show how this model can be adapted to deal with shadowing by fitting the statistical model to image brightness data using robust statistics. I will also show to to adapt the process to deal with non-Lambertian reflectance, through fitting a reflectance model that can capture the behavour of both shiny and rough surfaces. Finally, I will show how the shape information delivered by the process can be used to perform face recognition and gender determination. This talk will provide a synopsis of recent work by Smith and Hancock (PAMI 07, IJCV09, IJCV 2010) and Wu, Smith and Hancock (IVC 2010)

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