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This paper aims to explore the optimal feature selection with dimensionality reduction and jointly sparse representation scheme for classification. The proposed method is called Optimal Feature Selection Classification (OFSC). Our model simultaneously learns an orthogonal subspace for jointly sparse feature selection and representation via l2,1-norms regularization. To solve the proposed model, an...
The `displacement expert' has recently proven popular for rapid tracking applications. In this paper, we note that experts are typically constrained only to produce approximately correct parameter updates at training locations. However, we show that incorporating constraints on the gradient of the displacement field within the learning framework results in an expert with better convergence and fewer...
Putting forward a face recognition method based on Diagonal Principal Component Analysis and BP neural network. Firstly, do the dimension reduction to the sample data and take the DiaPCA method to avoid the information drop; Then, use the classics BP neural network to do the face detection. It not only shorten the net training time, but also improve the accuracy of the recognition. It used 1000 face...
The matrix based data representation has been recognized to be effective for face recognition because it can deal with the undersampled problem. One of the most popular algorithms, the two dimensional linear discriminant analysis (2DLDA), has been identified to be effective to encode the discriminative information for training matrix represented samples. However, 2DLDA does not converge in the training...
In order to improve the training convergence speed and detection accuracy of diverse AdaBoostSVM, an improved algorithm is proposed according to the asymmetry in face detection. In the algorithm, the weight of each weak learner, which represents importance of each weak learner, is determined by the error rate and the recognition capability of the weak learner for the face samples. The results of the...
Maximum Margin Criterion is a well-known method for feature extraction and dimensionality reduction. In this paper, we propose a novel feature extraction method, namely Two Dimensional Maximum Margin Criterion (2DMMC), specifically for matrix representation data, e.g. images. 2DMMC aims to find two orthogonal projection matrices to project the original matrices to a low dimensional matrix subspace,...
Facial expression recognition can be divided into three steps: face detection, expression feature extraction and expression categorization. Facial expression feature extraction and categorization are the most key issue. To address this issue, we propose a method to combine local binary pattern (LBP) and embedded hidden markov model (EHMM), which is the key contribution of this paper. This paper first...
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