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A 3-stage algorithm is proposed for automatic detection of the four primary landmarks in 3D face imagery: eyes, nose, and mouth. Pose and facial expression variations which raise major difficulties in landmark processing are the primary focus of this work. In the first stage, Gaussian and Mean curvatures are used to extract ridge and valley points. The second stage utilizes a recursive grouping algorithm...
Consider a homoscedastic multi-class Gaussian classification problem where the class mean vectors and the common covariance matrix are not known to the practitioner. Rather, they are estimated from given sample vectors available for each class. In this paper, an empirical procedure for approximating the bias of the holdout estimator of the Bayesian error probability (BEP) is presented. Synthetic experiments...
In this work, we propose a new and novel framework for improving the performance of linear feature extraction (LFE) algorithms, characterized by the Bayesian error probability (BEP) in the extracted feature domain. The proposed framework relies on optimizing a tight quadratic approximation to the BEP in the transformed space with respect to the transformation matrix. Applied to many synthetic multi-class...
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