The success of sparse representation in image reconstruction triggers the research on sparse representation-based pattern classification (SRC). A strict and standard dictionary creates sparse coefficients

but may not achieve a better classification accuracy in the SRC model. Recent research reveals that the collaborative representation (CR) mechanism

but not the L-norm sparsity constraint

improves face recognition (FR) accuracy. Therefore

constructing a rational and optimal dictionary for SRC model is a challenging task. We propose a novel SRC fusion method using a dynamical class-elimination mechanism

which strengthens the ability of collaborative representation

and a greedy search (GS) strategy for face recognition. The proposed method involves two aspects: training variable selection and classification strategy and sparse coding coefficient decomposition. The training variable selection and classification strategy aims to represent a query sample as a linear combination of the most informative training samples and to exploit an optimal representation of the training samples from the classes with major relevant contributions. Instead of eliminating several classes at one time

we eliminate classes one by one with GS sparse coding process until the ideal number of classes is obtained. In the context of the proposed method

an important goal is to select a subset of variables that can accomplish the provision of a descriptive representation for sparse category knowledge structure. We develop a heuristic learning strategy to achieve this goal. The method converts the original classification problem into a simpler one that contains a relatively small number of classes. The remaining final training samples are used for classification and to produce the best representation of the test sample. Literature validates that the CR mechanism has a significant role in the SRC scheme compared with the sparsity constraint. However

the sparsity constraint cannot be removed from the SRC scheme. We introduce a greedy search method

i.e.

error-constrained OMP

to integrate sparsity and CR mechanism and to solve the problem of sparse decomposition. Term regularization has a twofold role. First

it makes the least square solution stable; and second

it introduces a certain amount of “sparsity” to the solution. However

this sparsity is weaker than that by L norm. The SRC model shows that the test samples are not represented by all training samples

but can be sparsely represented over a dictionary. Therefore

we follow the following two key points: CR mechanism to improve classification and the condition of sparse constraint. For the SRC analysis

a test sample has been represented as a linear combination of all the training samples. The coefficient of a training sample in the linear combination acts as the weight of the training sample. The method assigns the test sample into the class that produces the minimum representation residual. A smaller coefficient denotes that some training samples have fewer contributions. Therefore

these training samples are inconclusive to the classification. The class that has fewer contribution to represent the test sample can be assigned to a zero coefficient

and the linear combination of all the remaining training samples is reassessed. These remaining training samples are informative to exploit an optimal representation of the test sample with major relevant contributions. The proposed method removes only one candidate class from all classes. The removed class is far from the test sample of the linear combination that will represent the test sample during iteration. The classes that are very far from the test sample are first excluded

and the classification decision depends only on the remaining classes. Experiments conducted on the ORL

FERET

and AR face databases have demonstrated the effectiveness of the proposed method

and the recognitions rates are up to 97.88%

67.95%

and 94.50%

respectively. A novel SRC fusion method using hierarchical multi-scale LBP and greedy search strategy is developed. The method aims to establish a heuristic iterative algorithm to obtain the supervised sparse representation of the test sample and then exploit an optimal representation of training samples from the classes with major relevant contributions. This method involves two aspects: dynamical class-elimination mechanism and replacement of the sparse approximation problem by a greedy search strategy. The test sample is classified into a class whose contribution has the minimum error. In this method

the sparse factors exerting influence on the structure knowledge can be incorporated into the sparse representation via iterative measurement of sparsity constraint. Experimental results show the feasibility and effectiveness of the proposed method. This type of sparse learning method is a new attempt compared with traditional popular representation methods in image recognition and can be clearly interpreted and be successfully applied in classifications. Future work can place emphasis on the quality of dictionary leaning. A study on the effect of introducing fuzzy weights into each atom will give them a varying fuzzy degree of popularity.