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Fast and Exact Newton and Bidirectional Fitting of Active Appearance Models
2017
IEEE Transactions on Image Processing
Active Appearance Models (AAMs) are generative models of shape and appearance that have proven very attractive for their ability to handle wide changes in illumination, pose and occlusion when trained in the wild, while not requiring large training dataset like regression-based or deep learning methods. The problem of fitting an AAM is usually formulated as a non-linear least squares one and the main way of solving it is a standard Gauss-Newton algorithm. In this paper we extend Active
doi:10.1109/tip.2016.2642828
pmid:28026767
fatcat:idqvbz2qkbafrithebmycflicm