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基于核的多元区别分析算法的特征抽取及其在人脸识别中的应用 被引量：2

Optimal feature extraction and face recognition based on kernel machine-based one-parameter multiple discriminant analysis
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摘要 提出了一种基于核技术的求多元区别分析最佳解的K1PMDA算法,并把这一算法应用于人脸识别中。对线性人脸识别中存在两个突出问题:1、在光照、表情、姿态变化较大时,人脸图像分类是复杂的、非线性的;2、小样本问题,即当训练样本数量小于样本特征空间维数时,导致类内散布矩阵奇异。对于前一个问题,可以采用核技术提取人脸图像样本的非线性特征,对于后一个问题,采用加入一个扰动参数的扰动算法。通过对ORL,YaleGroupB以及UMIST三个人脸库的实验表明,该算法是可行的、高效的。 A new algorithm, namely kernel machine-based one-parameter multiple discriminant analysis （K1PMDA）, to extract optimal discriminant features was proposed and applied to face recognition. There are two problems in linear face recognition： One is that the distribution of face images with different pose, illumination and face expression is complex and nonlinear. The other is the small sample size （S3） problem. Tnis problem occurs when the number of training samples is smaller than the dimeusionality of feature vectors, which results in a singular within-class scatter matrix. For the former, kernel technique can be used to extract nonlinear feature, and for the latter, a disturbed parameter was introduced to overcome S3 problem. Three databases, namely ORL, Yale Group B, and UMIST were selected for evaluation. The results are encouraging.
作者 吕冰 王士同
机构地区 江南大学信息工程学院
出处 《计算机应用》 CSCD 北大核心 2006年第11期2781-2783,2786,共4页 journal of Computer Applications
关键词 核技术 多元区别分析 小样本问题 人脸识别 kernel Multiple Discriminant Analysis（MDA） Small Sample Size（S3） face recognition
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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参考文献15

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共引文献9

同被引文献31

  • 1孙权森,曾生根,王平安,夏德深.典型相关分析的理论及其在特征融合中的应用[J].计算机学报,2005,28(9):1524-1533. 被引量：89
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引证文献2

二级引证文献4

计算机应用
2006年 第11期

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