计算机工程2012，Vol.38Issue(1)：163-164,167,3.DOI:10.3969/j.issn.1000-3428.2012.01.051
核邻域保持判别嵌入在人脸识别中的应用
Application of Kernel Neighborhood Preserving Discriminant Embedding in Face Recognition
摘要
Abstract
In order to have a data reduction more effectively, this paper proposes a new manifold learning algorithm named Kernel Neighborhood Preserving Discriminant Embedding(KNPDE) which puts kernel mapping into the Neighborhood Preserving Discriminant Embedding(NPDE). The algorithm adopts the difference of between within-class similarity matrix and between-class scatter matrix as the discriminant criterion. So it can avoid receiving the restraint of full rank of within-class scatter matrix. The algorithm is applied to the face recognition and solves the problem of nonlinear and small sample for face data. The experiment results on the ORL and Yale face database show that this algorithm has a good recognition performance.关键词
核方法/邻域保持判别嵌入/数据降维/流形字习/人脸识别Key words
kernel method/ Neighborhood Preserving Discriminant Embedding(NPDE)/ data dimension reduction/ manifold learning/ face recognition分类
信息技术与安全科学引用本文复制引用
王燕,白万荣..核邻域保持判别嵌入在人脸识别中的应用[J].计算机工程,2012,38(1):163-164,167,3.基金项目
甘肃省自然科学基金资助项目(1014RJZA009) （1014RJZA009）
