| 奇异值分解在人脸识别中的应用 |
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| 引用本文： | 何婧,冯国灿.奇异值分解在人脸识别中的应用[J].广东教育学院学报,2006,26(3):92-96. |
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| 作者姓名： | 何婧 冯国灿 |
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| 作者单位： | [1]华南农业大学理学院,广东广州510642 [2]中山大学数学与计算科学学院,广东广州510275 |
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| 基金项目： | 教育部科学技术研究项目;广东省自然科学基金 |
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| 摘 要： | 使用人脸数字图像奇异值分解中前面部分较大的奇异值及其对应的特征向量来重构图像，以剔除原图像中由于光照、表情、姿势等噪声影响对应的高频信息，并将重构图像作为模板进行识别．此方法无需使用较多的奇异值和训练样本，就能达到了很高的识别率，大大降低了识别工作量，并优于PCA方法．
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| 关 键 词： | 奇异值分解 人脸识别 PCA方法 |
| 文章编号： | 1007-8754(2006)03-0092-05 |
| 收稿时间： | 12 28 2005 12:00AM |
| 修稿时间： | 2005年12月28 |
The Application of Singular Value Decomposition in Face Recognition |
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| HE Jing,FENG Guo-can.The Application of Singular Value Decomposition in Face Recognition[J].Journal of Guangdong Education Institute,2006,26(3):92-96. |
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| Authors: | HE Jing FENG Guo-can |
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| Institution: | 1. School of Science, South China Agriculture University, Guangzhou, Guangdong, 510642, P. R. China; 2. School of Mathematics Computational Science, Zhongshan University, Guangzhou, Guangdong, 510275, P. R. China |
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| Abstract: | In this paper, those bigger singular values and their corresponding eigenvectors are employed as features while others are excluded as the high frequency information disturbed by the illumination, such as the expression, the pose or other noise. Experimental results suggest that this novel method works well and the recognition rates are higher than that of eigenface based on PCA. |
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| Keywords: | singular value decomposition face recognition PCA |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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