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

Publication Type:
Journal Article
Citation:
Journal of Southeast University (English Edition), 2016, 32 (1), pp. 35 - 38
Issue Date:
2016-03-01
Full metadata record
Files in This Item:
Filename Description Size
SubmittedVersion.pdfSubmitted Version268.05 kB
Adobe PDF
© 2016, Editorial Department of Journal of Southeast University. All right reserved. 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).
Please use this identifier to cite or link to this item: