| 一种基于空间变换的核Fisher鉴别分析 |
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| 引用本文： | 陈才扣,高林,杨静宇.一种基于空间变换的核Fisher鉴别分析[J].计算机工程,2005,31(8):17-18,60. |
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| 作者姓名： | 陈才扣 高林 杨静宇 |
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| 作者单位： | 1. 南京理工大学计算机科学系,南京,210094;扬州大学计算机科学与工程系,扬州,225009
2. 南京理工大学计算机科学系,南京,210094 |
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| 基金项目： | 国家自然科学基金资助项目(60072034)，国家教委博士点基金资助项目 |
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| 摘 要： | 引入空间变换的思相想，提出了一种基于空间变换的核Fisher鉴别分析，与KFDA不同的是，该方法只需在一个较低维的空间内执行，从而较大幅度地降低了求解最优鉴别矢量集的计算量，提高了计算速度，在ORL标准人脸库上的试验结果验证了所提方法的有效性。
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| 关 键 词： | 空问变换 核Fisher鉴别分析 特征抽取 人脸识别 |
| 文章编号： | 1000-3428(2005)08-0017-02 |
Space Transformation-based Kernel Fisher Discriminant Analysis |
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| CHEN Caikou,GAO Lin,YANG Jingyu.Space Transformation-based Kernel Fisher Discriminant Analysis[J].Computer Engineering,2005,31(8):17-18,60. |
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| Authors: | CHEN Caikou GAO Lin YANG Jingyu |
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| Affiliation: | CHEN Caikou1,2,GAO Lin1,YANG Jingyu1 |
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| Abstract: | Considering the above weaknesses, based on the idea of space transformation, a fast algorithm for extraction of the optimal nonlinear discriminant vectors in high dimensional feature space is presented. Unlike the kernel fisher discriminant analysis (KFDA) method, the proposed algorithm only need to perform in a low dimensional transformed space, which leads to significant computational reduction. The experimental results on ORL face database verify that the presented approach is faster than KFDA in terms of recognition time while retain their accuracy. |
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| Keywords: | Space transformation Kernel Fisher discriminant analysis Feature extraction Face recognition |
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