Deformable Templates for Face Description, Recognition, Interpretation, and Learning

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9317670 Yuille This is the first year funding of a three-year continuing award to investigate the problems of robust face recognition under lighting, albedo, geometry and viewpoint variations by formulating new modeling and matching alogrithms. The approach consists of deriving sets of models which, when interpolated, serve to handle wide variations in viewpoint. The model used consists of two parts: first, a geometric model related to spatial variations and individual and expression changes, and second, an imaging model, which handles variations in lighting and albedo. The research involves the following four goals: 1) the design, test, and implementation of algorithms to match the model described against frontally-viewed faces, 2) the extension of deformable template techniques for tracking faces and their features and determine an interpretative mapping in terms of expressions, 3) the development of probabilistic methodologies for first learning from a database the prior distributions used by deformable templates, and later, to develop a learning method to determine the maximum a posteriori estimate of the model, and 4) to generalize the model to detect multiple faces in complex visual scenes, and perform robustly even in the presence of outliers and occluders.