摘要
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.
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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2014年第1期77-81,共5页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.60632050,61373062,61373063)
江苏省普通高校研究生科研创新计划项目(No.CXZZ12_0204)资助
关键词
人脸识别
特征提取
流形
分类概率
鉴别分析
Face Recognition, Feature Extraction, Manifold, Classification Probability, DiscriminantAnalysis
