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| Feature Extraction Method on Maximum Margin Criterion with Locality Preserving |
| WANG Chao, WANG Shi-Tong |
| School of Information Technology, Jiangnan University, Wuxi 214122 |
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Abstract The maximum margin criterion (MMC) aims at maximizing the inter-class scatter and minimizing the intra-class scatter simultaneously after the projection to overcome the small sample size problem. A feature extraction method is proposed. Compared with the original MMC method, the proposed method can manifold local structure information better by multiplying the defined weight and regulating the parameter. The experimental results on ORL face database ,YALE database and UMIST database show that the proposed method is robust to illumination and pose, and it improves the recognition rate and recognizes the face images efficiently.
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Received: 24 November 2008
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[1] Yu Hua, Yang Jie. A Direct LDA Algorithm for High-Dimensional Data with Application to Face Recognition. Pattern Recognition, 2001, 34(10): 2067-2070
[2] Zhuang Zhemin, Zhang Aniu, Li Fenlan. Based on an Optimized LDA Algorithm for Face Recognition. Journal of Electronics & Information Technology, 2007, 29(9): 2047-2049 (in Chinese)
(庄哲民,张阿妞,李芬兰.基于优化的LDA算法人脸识别研究.电子与信息学报, 2007, 29(9): 2047-2049)
[3] Nelhumeur P N, Hespanha J P, Kriegmen D J. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720
[4] Li Haifeng, Jiang Tao, Zhang Keshu. Efficient and Robust Feature Extraction by Maximum Margin Criterion. IEEE Trans on Neural Networks, 2006, 17(1): 157-165
[5] Li Zhiyong, Yang Jinyu, Zheng Yujie, et al. New and Efficient Feature Extraction Methods Based on Maximum Margin Criterion. Journal of System Simulation, 2007, 19(5): 1061-1066 (in Chinese)
(李智勇,杨靖宇,郑宇杰,等.基于最大间距准则(MMC)新的有效特征提取方法.系统仿真学报, 2007, 19(5): 1061-1066)
[6] He Xiaofei, Niyogi P. Locality Preserving Projections // Thrun S, Saul L K, Schlkopf B, eds. Neural Information Processing System. New York, USA: MIT Press, 2003, 16: 153-160
[7] He Xiaofei, Yan Shuicheng, Hu Yuxiao, et al. Face Recognition Using Laplacianfaces. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340
[8] Zhu Lei, Zhu Shan'an. Face Recognition Based on a New Feature Extraction Method. Opto-Electronic Engineering, 2007, 34(6): 122-125 (in Chinese)
(祝 磊,朱善安.识别的一种新的特征提取方法.光电工程, 2007, 34(6): 122-125)
[9] Sun Tao, Gu Shiwen, Fei Yaoping. A Comparative Study on Face Recognition Using PCA-Based Methods. Modern Electronics Technique, 2007, 30(1): 112-114 (in Chinese)
(孙 涛,谷士文,费耀平.基于PCA算法的人脸识别方法研究比较.现代电子技术, 2007, 30(1): 112-114)
[6] Zhang Zhiwei, Yang Fan, Xia Kewei, et al. A Supervised LPP Algorithm and Its Application to Face Recognition. Journal of Electronics & Information Technology, 2008, 30(3): 539-541 (in Chinese)
(张志伟,杨 帆,夏克文,等.一种有监督的LPP算法及其在人脸识别中的应用.电子与信息学报, 2008, 30(3): 539-541) |
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2009, Vol. 22 
