robust large margin discriminant tangent analysis for face recognition
文献类型：期刊论文
| 作者 | Yang Nanhai ; He Ran ; Zheng Wei-Shi ; Wang Xiukun |
| 刊名 | Neural Computing and Applications
 |
| 出版日期 | 2012 |
| 卷号 | 21期号:2页码:269-279 |
| 关键词 | Algorithms Discriminant analysis Gaussian noise (electronic) Statistics Teaching |
| ISSN号 | 0941-0643 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 语种 | 英语 |
| 公开日期 | 2013-09-17 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/15175]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：软件研究所
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