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In this paper we investigate whether the 2.5D shape information delivered by a novel shape-from-shading algorithm can be used for illumination insensitive face recognition. We present a robust and efficient facial shape-fromshading algorithm which uses principal geodesic analysis to model the variation in surface orientation across a face. We show how this algorithm can be used to recover accurate facial shape and albedo from real world images. Our second contribution is to use the recovered 2.5D shape information in a variety of recognition methods. We present a novel recognition strategy in which similarity is measured in the space of the principal geodesic parameters. We also use the recovered shape information to generate illumination normalised prototype images on which recognition can be performed. Finally we show that, from a single input image, we are able to generate the basis images employed by a number of well known illumination-insensitive recognition algorithms. We also demonstrate that the principal geodesics provide an efficient parameterisation of the space of harmonic basis images.