CAJ | 학술논문

针对特征提取算法中存在的问题，在线性鉴别分析的基础上提出分类概率保持鉴别分析( CPPDA)并成功应用于人脸识别。 CPPDA首先计算每个样本的分类概率，并利用分类概率重新定义样本的类间散布矩阵和类内散布矩阵；然后通过最大化类间散度同时最小化类内散度寻求最佳投影矩阵，使得样本的原始分布信息在低维特征空间能得到保持。在ORL、Yale及FERET人脸库上进行测试比较，结果表明文中所提方法的优越性。
침대특정제취산법중존재적문제，재선성감별분석적기출상제출분류개솔보지감별분석( CPPDA)병성공응용우인검식별。 CPPDA수선계산매개양본적분류개솔，병이용분류개솔중신정의양본적류간산포구진화류내산포구진；연후통과최대화류간산도동시최소화류내산도심구최가투영구진，사득양본적원시분포신식재저유특정공간능득도보지。재ORL、Yale급FERET인검고상진행측시비교，결과표명문중소제방법적우월성。
To solve the problems in feature extraction algorithms, an algorithm based on linear discriminant analysis ( LDA) , called classification probability preserving discriminant analysis ( CPPDA) , is proposed for face recognition. Firstly, the classification probability of each sample is computed by CPPDA, and both the between-class scatter matrix and the within-class scatter matrix are redefined by the classification probability. Secondly, through maximizing the between-class scatter and minimizing the within-class scatter simultaneously, an optimal projection matrix can be preserved in the low-dimensional feature space, such as the distribution information contained in the original data. Finally, the experimental results on the ORL,Yale and FERET face databases demonstrate the superiority of the proposed algorithm compared with other algorithms.