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计算机工程 ›› 2006, Vol. 32 ›› Issue (22): 203-205. doi: 10.3969/j.issn.1000-3428.2006.22.073

• 人工智能及识别技术 • 上一篇    下一篇

应用小指数多项式的KPCA+零空间人脸识别

郭 恺,付永生,冷 严,侯 剑   

  1. (山东大学信号与信息处理研究所,济南 250100)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2006-10-20 发布日期:2006-10-20

Face Recognition Combining Null Space Approach and Kernel PCA Including Fractional Power Polynomial Models

GUO Kai, FU Yongsheng, LENG Yan, HOU Jian   

  1. (Institute of Signal and Information Processing, Shandong University, Jinan 250100)
  • Received:1900-01-01 Revised:1900-01-01 Online:2006-10-20 Published:2006-10-20

摘要: 利用小指数多项式核主分量分析(KPCA)提取人脸样本的非线性特征,提高对光照、姿态及面部表情变化的鲁棒性,构造训练样本的类内散布矩阵零空间,在此零空间内找到令类间离散度最大的投影方向,往此方向投影得到人脸样本的最优分类特征矢量。实验结果表明;该方法的识别率和对光照、姿态及面部表情变化的鲁棒性比Fisher脸方法有显著提高。

关键词: 人脸识别, 小指数多项式, 核主分量分析, 零空间

Abstract: This paper presents a novel KPCA+Null Space method by integrating the kernel PCA method and the null space of the within-class scatter matrix. The kernel PCA method which extends to include fractional power polynomial models first derives nonlinear features of face samples, then this paper constructs the null space of the within-class scatter matrix, and calculates the optimal discriminating vectors by maximizing the between-class distribution, after the projection of the samples onto the optimal discriminating vectors, it can obtain the optimal discriminating feature vectors. The test results show that the KPCA+Null Space method is superior to Fisher face method in terms of recognition accuracy and stability to the variations between the images of the same face due to illumination, expression and viewing direction.

Key words: Face recognition, Fractional power polynomial models, Kernel principal component analysis(KPCA), Null space