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In this paper, the principles of sparse signal representation theory are explored in order to perform facial expressions recognition from frontal views. Motivated by the success such methods have demonstrated in the face recognition problem, we formulate the feature extraction procedure in order to achieve facial expression recognition as an l1 optimization problem. We show that the straightforward...
In this paper general solutions for nonlinear nonnegative component analysis for data representation and recognition are proposed. That is, motivated by a combination of the Nonnegative Matrix Factorization (NMF) algorithm and kernel theory, which has lead to an NMF algorithm in a polynomial feature space, we propose a general framework where one can build a nonlinear nonnegative component analysis...
We propose a novel set of features extracted using Hybrid Discriminant Analysis (HDA) for use in a facial expression recognition task. HDA is formulated by combining the optimization problems which are solved in Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) methods. By tuning weighting parameters, new sets of discriminant directions are obtained. Using these directions...
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