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Paul Koppen

Paul Koppen, PhD, is the Project Manager for a major collaborative research project in face recognition FACER2VM, funded by the Engineering and Physical Sciences Research Council. He is based at the University of Surrey, where he studied for a PhD degree in 3D face analysis. After his PhD he was employed as a postdoctoral research fellow working on a project relating face shape to genetics, funded by the Wellcome foundation. More recently he had responsibility for technology transfer in the area of 3D face model building and 3D face morphable model fitting.

Talk: 3D morphable face model and its applications

3D Morphable Face Models (3DMM) have been used in face recognition for some time now. They can be applied in their own right as a basis for 3D face recognition and analysis involving 3D face data. However their prevalent use over the last decade has been as a versatile tool in 2D face recognition to normalise pose, illumination and expression of 2D face images. It has the generative capacity to augment the training and test databases for various 2D face processing related tasks. It can expand the gallery set for pose invariant face matching. For any 2D face image it can furnish complementary information, in terms of its 3D face shape and texture. It can also aid multiple frame fusion by providing the means of registering a set of 2D images. A key enabling technology for this versatility is 3D face model to 2D face image fitting. The recent developments in 3D model to 2D image fitting will be discussed. They include the use of symmetry to improve the accuracy of illumination estimation, multistage close form fitting to accelerate the fitting process, modifying the imaging model to cope with 2D images of low resolution, and building albedo 3DMM. These various enhancements will be overviewed and their merit demonstrated on a number of face analysis related problems.