Enhanced kernel minimum squared error algorithm and its application in face recognition

To improve the classification performance of the kernel minimum squared error (KMSE), an enhanced KMSE algorithm (EKMSE) is proposed. It redefines the regular objective function by introducing a novel class label definition, and the relative class label matrix can be adaptively adjusted to the kernel matrix. Compared with the common methods, the new objective function can enlarge the distance between different classes, which therefore yields better recognition rates. In addition, an iteration parameter searching technique is adopted to improve the computational efficiency. The extensive experiments on FERET and GT face databases illustrate the feasibility and efficiency of the proposed EKMSE. It outperforms the original MSE, KMSE, some KMSE improvement methods, and even the sparse representation-based techniques in face recognition, such as collaborate representation classification (CRC).