支持向量鉴别分析及在人脸表情识别中的应用  

Support Vector Discriminant Analysis and Its Application to Facial Expression Recognition

作　　者：应自炉[1] 唐京海[1] 李景文[1] 张有为[1] 

机构地区：[1]北京航空航天大学电子信息工程学院

出　　处：《电子学报》2008年第4期725-730,共6页Acta Electronica Sinica

摘　　要：模式识别一般首先要对数据进行降维,PCA和LDA及其对应的核化算法是其中应用广泛的方法,但这些算法的应用前提是假设样本数据为高斯分布,在少样本训练时它们的推广性能有很大局限.本文提出了一种基于支持向量机的鉴别分析算法,该算法首先寻找有限样本情况下最优分类面,以其法线方向为投影轴对数据进行投影降维,在多类情况下提供了极其丰富的方案选择投影轴.该算法体现了支持向量机的内在优良推广性能,克服了PCA和LDA等算法的局限性.本文将所提算法应用于人脸表情特征提取,并与PCA、LDA、KPCA、GDA等算法进行了比较,结果表明该算法的有效性.Dimension reduction of data is usually an important preprocessing step in pattern recognition. PCA and Fisher＇s LDA and their kemelized versions are widely used approaches for dimension reduction. But they have limitations when used for small sample training because of their Gaussian distribution assumption. This paper propose an algorithm for dimension reduction called support vector discriminant analysis （SVDA）, which first looks for the optimal separating hyperplane by SVM algorithm and then project data in the corresponding normal direction. In multiclass cases, the algorithm has many choices for selecting projecting axis. The algorithm has the intrinsic nice generalization ability of SVM. The paper appfies the algorithm to the feature extraction in facial expression recognition application and compares the results to other algorithms such as PCA,LDA,KPCA and GDA. The results show the effectiveness of the proposed algorithm.

关 键 词：模式识别 主元分析 FISHER鉴别分析 支持向量机 表情识别 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]