|Table of Contents|

[1] Zhao Yingnan, , He Xiangjian, et al. Enhanced kernel minimum squared error algorithmand its application in face recognition [J]. Journal of Southeast University (English Edition), 2016, 32 (1): 35-38. [doi:10.3969/j.issn.1003-7985.2016.01.007]

Enhanced kernel minimum squared error algorithmand its application in face recognition()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2016 1
Research Field:
Computer Science and Engineering
Publishing date:


Enhanced kernel minimum squared error algorithmand its application in face recognition
Zhao Yingnan1 2 3 He Xiangjian3 Chen Beijing1 2 Zhao Xiaoping1 2
1Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China
2School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
3School of Computing and Communications, University of Technology, Sydney, Sydney 2007, Australia
minimum squared error kernel minimum squared error pattern recognition 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).


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Biography: Zhao Yingnan(1973—), female, doctor, associate professor, ann_zhao_99@163.com.
Foundation items: The Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD), the National Natural Science Foundation of China(No.61572258, 61103141, 51405241), the Natural Science Foundation of Jiangsu Province(No.BK20151530), Overseas Training Programs for Outstanding Young Scholars of Universities in Jiangsu Province.
Citation: Zhao Yingnan, He Xiangjian, Chen Beijing, et al. Enhanced kernel minimum squared error algorithm and its application in face recognition[J].Journal of Southeast University(English Edition), 2016, 32(1):35-38. DOI:10.3969/j.issn.1003-7985.2016.01.007.
Last Update: 2016-03-20