Face recognition often suffers from the Small Sample Size problem. Regularization is one of the solutions to this problem. In this paper, we investigate the Kullback-Leibler information measure (KLIM) based regularization classifiers for face recognition. Two parameter estimation approaches including the cross-validation technique and model selection criterion are chosen to optimize the regularization parameter. In the experiments, the ORL face data is used to evaluate these algorithms. We

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... red the KLIM algorithms with quadratic discriminant analysis, linear discriminant analysis, regularized discriminant analysis, and leave-one-out covariance matrix estimate. Considering both time cost and classification rate, KLIM classifiers exceed the others and obtain stable results.