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—In this paper, A new approach to facial expression recognition is constructed by combining the support vector discriminant analysis (SVDA) and local binary pattern (LBP) operator. LBP is an effective low-cost image descriptor to extract facial texture representing expression features. The basic idea of SVDA is to find the projection axes according to the(More)
In this paper, A new approach to face recognition is constructed by combining the local binary pattern (LBP) operator and locally linear embedding (LLE). LBP is an effective low-cost image descriptor to extract facial texture feature which represents the local structure of face images. LLE is an excellent non-linear data dimensionality reduction method. Its(More)
A novel approach to facial expression recognition with Marginal Fisher Analysis (MFA) on Local Binary Pattern (LBP) is proposed. Firstly, each image is transformed by an LBP operator and then divided into 3x5 non-overlapping blocks. The features of facial expression images are formed by concatenating the LBP histogram of each block. Secondly, MFA algorithm(More)
In this paper, a novel discriminant analysis named two-dimensional Heteroscedastic Discriminant Analysis (2DHDA) is presented, and used for gender classification. In 2DHDA, equal within-class covariance constraint is removed. Firstly, the criterion of 2DHDA is defined according to that of 2DLDA. Secondly, the criterion of 2DHDA, log and rearranging terms(More)
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