一种新的基于最大散度差准则的特征抽取方法

A New Efficient Feature Extraction Method Based on Maximum Scatter Difference Criterion

本文基于最大散度差准则(MSDC),利用统计不相关投影空间,提出了一组具有统计不相关性的最佳鉴别矢量的计算方法。该方法的目标是寻求一组鉴别矢量集,既要使投影后的特征空间的类间散度最大,而类内散度最小;又要使最佳鉴别矢量之间具有统计不相关性。另外,本文还揭示了最大散度差鉴别准则与Fisher准则的内在关系。在ORL与NUST603人脸库上的实验结果表明,本文所提出的方法在识别性能上优于原MSDC特征抽取方法与传统的PCA方法。

Making use of the statistical uncorrelated projection space, a new method of statistically uncorrelated optimal discriminant vectors is presented in this paper based on the maximum scatter difference discriminant criterion. The uncorrelated optimal discriminant vectors are obtained by resolving the orthogonal vectors based on maximum scatter difference discriminant criterion in the uncorrelated projection space. The purpose of the method is to maximum the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection, and eliminate the statistically correlation between features. Besides, this paper reveals the relation between the maximum scatter difference discriminant criterion and Fisher criterion for feature extraction. Experimental results on ORL and NUST603 face database show the effectiveness of the proposed algorithm. The recognition rate of the method is superior to MSDC and PCP.