东南大学学报（英文版）2016，Vol.32Issue(1)：35-38,4.DOI:10.3969/j.issn.1003-7985.2016.01.007
增强 KMSE及人脸识别应用
Enhanced kernel minimum squared error algorithm and its application in face recognition
摘要
Abstract
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 .关键词
最小均方误差/核最小均方误差/模式识别/人脸识别Key words
minimum squared error/kernel minimum squared error/pattern recognition/face recognition分类
计算机与自动化引用本文复制引用
赵英男,何祥健,陈北京,赵晓平..增强 KMSE及人脸识别应用[J].东南大学学报（英文版）,2016,32(1):35-38,4.基金项目
The Priority Academic Program Development of Jian－gsu Higher Education Institutions PAPD the National Natural Science Foundation of China No．615722586110314151405241 the Natu－ral Science Foundation of Jiangsu Province No．BK20151530 Over－seas Training Programs for Outstanding Young Scholars of Universities in Jiangsu Province． （）
