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Face gender recognition is a very challenging problem in computer vision, which plays an important role in many visual applications. In this paper, we present a framework that combines the unsupervised dictionary learning and supervised classifier training together to this gender recognition problem. We firstly apply sparse non-negative matrix factorization (sparse NMF) to learn intrinsic part-based...
Facial expression classification is a very challenging problem in machine perception, which plays an important role in many visual applications. In this paper, we use a machine-learning-based framework to address this problem. We firstly apply K-SVD to learn sparse representations of the face images in the training set in an unsupervised manner for image modeling. After that we train a SVM classifier...
Many of characteristics of support vector machine (SVM) are determined by the type of kernels used. Traditional kernels such as polynomial kernel and radial basis function kernel have many limitations. It is valuable to investigate the problem of whether a better performance could be obtained if we construct a scaling kernel by using the scaling function. This paper presents a way for building a wavelet-based...
Multiresolution signal approximation (MSA) provides a simple hierarchical approximation of the signals. And support vector machine (SVM) has been introduced as a novel tool for solving approximation problems. Based on the fact that scale subspaces onto which MSA projects the signals are reproducing kernel Hilbert spaces (RKHS), we integrate the approximation criterion of SVM into MSA and then an SVM...
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