Kernel sparse representation-based classifier ensemble for face recognition[J] . Li Zhang,Wei-Da Zhou,Fan-Zhang Li.Multimedia Tools and Applications . 2015 (1)L. Zhang, W.-D. Zhou, and F.-Z. Li, "Kernel sparse representation-based classifier ensemble for face recog- nition," Multimedia ...
The main contributions of this paper are: (1) A discriminant dictionary learning technique is proposed to overcome the limitation of the traditional Sparse Representation based classifier (SRC). (2) Context features are incorporated into SRC to refine the prostate boundary in an iterative scheme. (...
In this paper, a method of kernel optimization by maximizing a measure of class separability in the empirical feature space with sparse representation-based classifier (SRC) is proposed to solve the problem of automatically choosing kernel functions and their parameters in kernel learning. The ...
In this paper, we propose a sparse representation-based classification method using iterative class elimination strategy for face recognition. The proposed method aims to represent a test sample as a linear combination of the most competitive training samples and exploits an optimal representation of tra...
The sparse representation based classification method can be divided into two categories: holistic approaches and local feature-based approaches. In spite of the significant success in face recognition, improvements on higher robustness or lower computational complexity are still necessary for its real appl...
Expression robust three-dimensional face recognition based on Gaussian filter and dual-tree complex wavelet transform Dual-Tree Complex Wavelet TransformRejection ClassifierSparse Representation Based ClassificationSummary: In this paper, a fully automatic framework is proposed ... X Wang,Q Raun,J Yi,....
In recent years, sparse representation of image signals has been widely concerned in the field of pattern recognition. Wright J first proposed a sparse representation-based classifier (SRC); it constructed an overcomplete dictionary with multiple kinds of training samples with label information and clas...
First, we need to build a dictionary to represent the test samples, and then use the sparse representation coefficient and classification dictionary to identify the objects. Wright et al., for the first time, put forward a sparse representation-based classifier (SRC) [1]. SRC used the ...
The proposed method is compared with six representative emotion classification methods, including linear discriminant classifier, K-nearest-neighbor, radial basis function neural networks, support vector machines, sparse representation-based classification and kernel sparse representation-based classification. ...
Local feature-based methods gain importance in the recent years due to their robustness under degradation conditions. In this paper, a novel high-order local pattern descriptor in combination with sparse representation based classifier (SRC) is proposed for expression robust 3D face recognition. 3D ...