[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]
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Enhanced kernel minimum squared error algorithmand its application in face recognition(
)
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 32
- Issue:
- 2016 1
- Page:
- 35-38
- Research Field:
- Computer Science and Engineering
- Publishing date:
- 2016-03-20
Info
- Title:
- Enhanced kernel minimum squared error algorithmand its application in face recognition
- Author(s):
- 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
- Keywords:
- minimum squared error; kernel minimum squared error; pattern recognition; face recognition
- PACS:
- TP391
- DOI:
- 10.3969/j.issn.1003-7985.2016.01.007
- 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).
References:
[1] Muller K, Mika S, Ratsch G, et al. An introduction to kernel-based learning algorithms [J]. IEEE Transactions on Neural Networks, 2001, 12(2): 181-202. DOI:10.1109/72.914517.
[2] Xu Jianhua, Zhang Xuegong, Li Yanda. Kernel MSE algorithm: a unified framework for KFD, LS-SVM and KRR [C]//IEEE International Joint Conference on Neural Networks. Washington, DC, USA, 2001:1486-1491.
[3] Xu Yong, Zhang David, Yang Jian, et al. A two-phase test sample sparse representation method for use with face recognition [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2011, 21(9): 1255-1262.
[4] Zhang Lei, Yang Meng, Feng Xiangchu. Sparse representation or collaborative representation: Which helps face recognition? [C]//IEEE International Conference on Computer Vision. Barcelona, Spain, 2011: 471-478.
[5] Qi Zhu. Reformative nonlinear feature extraction using kernel MSE [J]. Neurocomputing, 2010, 73(16/17/18): 3334-3337. DOI:10.1016/j.neucom.2010.04.007.
[6] Zhao Yongping, Sun Jianguo, Du Zhonghua, et al. Pruning least objective contribution in KMSE [J]. Neurocomputing, 2011, 74(17): 3009-3018. DOI:10.1016/j.neucom.2011.04.004.
[7] Zhao Yongping, Wang Kangkang, Liu Jie, et. al. Incremental kernel minimum squared error(KMSE)[J]. Information Sciences, 2014, 270: 92-111. DOI:10.1016/j.ins.2014.02.117.
[8] Xu Yong, Yang Jingyu, Jin Zhong, et al. A learning approach to derive sparse kernel minimum square error model [C]//IEEE International Conference on Control and Automation.Guangzhou, China, 2007: 1278-1283.
[9]Wang Jinhua. A novel solution scheme for the kernel MSE model[C]//International Conference on Artificial Intelligence and Computational Intelligence. Shanghai, China, 2009: 375-378.
[10] Counterdrug Technology Development Program. The FERET database[EB/OL].(2004-06-16)[2016-01-30]. http://www.itl.nist.gov/iad/humanid/feret.
[11] Geogia Institute of Technology. Georgia Tech face database[EB/OL].(2010-01-01)[2016-01-30]. http://www.anefian.com/research/face_reco.htm.
Memo
- Memo:
-
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.