自动化学报2012，Vol.38Issue(9)：1503-1512,10.DOI:10.3724/SP.J.1004.2012.01503
二维投影非负矩阵分解算法及其在人脸识别中的应用
2-dimensional Projective Non-negative Matrix Factorization and Its Application to Face Recognition
摘要
Abstract
Face recognition algorithms through minimizing the loss function of non-negative matrix factorization must simultaneously calculate the base matrix and the coefficient matrix, which leads to the high computational complexity. This paper introduces the non-negative properties into 2-dimensional principal component analysis (2DPCA), and then proposes a novel 2-dimensional projective non-negative matrix factorization (2DPNMF) for face recognition. 2DPNMF preserves the local structure of face images but breaks through the restriction of minimizing the loss function of non-negative matrix factorization. Since 2DPNMF onfy needs calculating the projection matrix (base matrix), its computational complexity is greatly reduced. This paper theoretically proves the convergence of the proposed algorithm and uses YALE face database, FERET face database, and AR face database for the comparison experiments. Experimental results show that 2DPNMF has higher recognition performance as well as a much faster speed than NMF and 2DPCA.关键词
二维主成分分析/非负矩阵分解/人脸识别/特征提取Key words
2-dimensional principal component analysis (2DPCA), non-negative matrix factorization (NMF), face recognition, feature extraction引用本文复制引用
方蔚涛,马鹏,成正斌,杨丹,张小洪..二维投影非负矩阵分解算法及其在人脸识别中的应用[J].自动化学报,2012,38(9):1503-1512,10.基金项目
国家自然科学基金(60975015,61173131),重庆市科技攻关重点项目(CSTC2009AB2230),重庆市攻关项目(2009AC2057)资助 （60975015,61173131）
