中文摘要：

为了提高核最小均方误差（KMSE）方法的识别能力,提出一种增强KMSE方法（EKMSE）.该方法重新定义KMSE目标函数,引入一个新的类别标签定义,并使该定义下的类别标签矩阵能够随核矩阵自适应调整.与通常的目标函数相比,它能够使不同类别之间的距离增大,进而提高识别率.同时该算法在参数搜索中采用了迭代技术,有效提高了算法的计算效率.在FERET和GT人脸库上进行了充分的实验,结果表明EKMSE算法可行有效.该算法不仅优于原MSE,KMSE以及KMSE改进算法,也优于目前脸识别中的基于稀疏算法的最新技术CRC算法.

英文摘要：

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 newobjective 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）.