计算机工程与应用2016，Vol.52Issue(7)：150-154,190,6.DOI:10.3778/j.issn.1002-8331.1508-0036
加权小波和流形正则化的NMF融合的人脸识别
Face recognition based on weighted wavelet decomposition and manifold regularized non-negative matrix factorization
摘要
Abstract
In order to improve the recognition performance by obtaining more sufficient face features, the method of weighted wavelet decomposition and manifold regularized non-negative matrix factorization is introduced to realize face recognition. Firstly, wavelet decomposition with its weighted high frequency is applied to extract the features of weighted high frequency component and low frequency component from training samples. Secondly, with maintaining potential geometric structures and local features of the face features, it uses manifold regularized non-negative matrix factorization to acquire final recognition characteristics. Lastly, nearest neighbor method is used to be classified and recognized. Comparing with the traditional method of non-negative matrix factorization, experimental results on ORL face databases and YALE face databases show that the recognition rate is about increased by 5% and computation time is quite shorter. Hence, the proposed method has less time consuming, as well as a better recognition performance.关键词
人脸识别/加权小波变换/非负矩阵分解/流形正则化Key words
face recognition/weighted wavelet decomposition/non-negative matrix factorization/manifold regularization分类
计算机与自动化引用本文复制引用
王晓华,孙小姣,李越..加权小波和流形正则化的NMF融合的人脸识别[J].计算机工程与应用,2016,52(7):150-154,190,6.基金项目
国家自然科学基金（No.61301276）；西安工程大学控制科学与工程学科建设经费资助（No.107090811）；西安工程大学博士科研启动金项目（No.BS1207）；陕西省级大学生创新创业训练计划项目（No.1571）。 （）
