Face recognition based on the quotient image method and sparse representation

The algorithm based on a sparse representation computed by L1-minimization achieved state-of-the-art performance. However, the prerequisite hypothesis in the algorithm is that the training samples from each subject can construct a linear subspace of the corresponding subject, which requires the enough images of each object to be used as training samples. The requirement of large training sets restricts its applications in face recognition. Therefore, the improved algorithm based on SRC is proposed. Firstly, the quotient image method is improved. Then the nine basis images of each subject are generated by the improved quotient image method. Lastly, the synthetic basis images are taken to act as training set to fulfill recognition task. The experimental results show that the proposed approach is feasible and effective.

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