基于SVM的MultiView人脸识别中KDDA的特征选择
Feature Selection By KDDA For SVM-Based MultiView Face Recognition
摘要：
处理高维数据的人脸识别等应用需要一种映射技术，该技术引入了具有增强的区分能力的低维特征的表示和能够对这些复杂特征进行分类的适当分类器。传统的线性判别分析方法的最优性准则与所得到的特征表示的分类能力没有直接关系。此外，他们的分类精度受到“小样本量”的问题，这是经常遇到的FR任务。在这篇简短的论文中，我们结合了非线性核映射的数据称为KDDA与支持向量机分类器，以有效和成本效益的方式来处理这两个缺点。这里提出的方法进行比较，在分类精度方面，其他常用的FR方法UMIST人脸数据库。结果表明，该方法的性能总体上优于传统的FR方法，如特征脸，费舍尔脸，D-LDA方法和传统的线性分类器。
英文摘要：
Applications such as face recognition that deal with high-dimensional data need a mapping technique that introduces representation of low-dimensional features with enhanced discriminatory power and a proper classifier, able to classify those complex features. Most of traditional Linear Discriminant Analysis suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the "small sample size" problem which is often encountered in FR tasks. In this short paper, we combine nonlinear kernel based mapping of data called KDDA with Support Vector machine classifier to deal with both of the shortcomings in an efficient and cost effective manner. The proposed here method is compared, in terms of classification accuracy, to other commonly used FR methods on UMIST face database. Results indicate that the performance of the proposed method is overall superior to those of traditional FR approaches, such as the Eigenfaces, Fisherfaces, and D-LDA methods and traditional linear classifiers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
