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| robust large margin discriminant tangent analysis for face recognition | |
| Yang Nanhai; He Ran; Zheng Wei-Shi; Wang Xiukun | |
| 2012 | |
| Source | Neural Computing and Applications
 |
| ISSN | 0941-0643 |
| Volume | 21Issue:2Pages:269-279 |
| English Abstract | Fisher's Linear Discriminant Analysis (LDA) has been recognized as a powerful technique for face recognition. However, it could be stranded in the non-Gaussian case. Nonparametric discriminant analysis (NDA) is a typical algorithm that extends LDA from Gaussian case to non-Gaussian case. However, NDA suffers from outliers and unbalance problems, which cause a biased estimation of the extra-class scatter information. To address these two problems, we propose a robust large margin discriminant tangent analysis method. A tangent subspace-based algorithm is first proposed to learn a subspace from a set of intra-class and extra-class samples which are distributed in a balanced way on the local manifold patch near each sample point, so that samples from the same class are clustered as close as possible and samples from different classes will be separated far away from the tangent center. Then each subspace is aligned to a global coordinate by tangent alignment. Finally, an outlier detection technique is further proposed to learn a more accurate decision boundary. Extensive experiments on challenging face recognition data set demonstrate the effectiveness and efficiency of the proposed method for face recognition. Compared to other nonparametric methods, the proposed one is more robust to outliers. © 2011 Springer-Verlag London Limited.; Fisher's Linear Discriminant Analysis (LDA) has been recognized as a powerful technique for face recognition. However, it could be stranded in the non-Gaussian case. Nonparametric discriminant analysis (NDA) is a typical algorithm that extends LDA from Gaussian case to non-Gaussian case. However, NDA suffers from outliers and unbalance problems, which cause a biased estimation of the extra-class scatter information. To address these two problems, we propose a robust large margin discriminant tangent analysis method. A tangent subspace-based algorithm is first proposed to learn a subspace from a set of intra-class and extra-class samples which are distributed in a balanced way on the local manifold patch near each sample point, so that samples from the same class are clustered as close as possible and samples from different classes will be separated far away from the tangent center. Then each subspace is aligned to a global coordinate by tangent alignment. Finally, an outlier detection technique is further proposed to learn a more accurate decision boundary. Extensive experiments on challenging face recognition data set demonstrate the effectiveness and efficiency of the proposed method for face recognition. Compared to other nonparametric methods, the proposed one is more robust to outliers. © 2011 Springer-Verlag London Limited. |
| Indexed Type | EI |
| Keyword | Algorithms Discriminant Analysis Gaussian Noise (Electronic) Statistics Teaching |
| Department | (1) School of Software Technology Dalian University of Technology 116620 Dalian China; (2) National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences 100190 Beijing China; (3) School of Information Science and Technology Sun Yat-sen University 510275 Guangzhou China |
| Language | 英语 |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15175 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Yang Nanhai,He Ran,Zheng Wei-Shi,et al. robust large margin discriminant tangent analysis for face recognition[J]. Neural Computing and Applications,2012,21(2):269-279. |
| APA | Yang Nanhai,He Ran,Zheng Wei-Shi,&Wang Xiukun.(2012).robust large margin discriminant tangent analysis for face recognition.Neural Computing and Applications,21(2),269-279. |
| MLA | Yang Nanhai,et al."robust large margin discriminant tangent analysis for face recognition".Neural Computing and Applications 21.2(2012):269-279. |
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