Geometry of face space
نویسنده
چکیده
The geometrical character of face space is investigated, and is analyzed within the framework of eigenface decompositions. To deal with the sparse data problem, which occurs when not enough data is present to fill the space, we construct and test a range of probabilistic models. A key tool in this investigation is the decomposition of the eigenfaces representation of face space into odd and even (in the face midline) components. As has been pointed out in the literature, lighting artifacts may account for most of the variance encountered in analyzing face space data. It is here demonstrated that most of the odd eigenfaces, and a very small set of even eigenfaces, are associated with the lighting artifact. On filtering out this set of eigenfaces in the analysis, we achieve near perfect recognition rates, even for faces exhibiting extreme lighting artifacts. It is also shown here that a multidimensional Gaussian model of face images is a moderately successful approximation in describing symmetrized face subspace. Random sampling of the Gaussian model for this subspace provides visually realistic synthetic faces, which respect gender. Index Terms Face and gesture recognition, computational models of vision, psychology, singular value decomposition.
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