2-dimensional Projective Non-negative Matrix Factorization and Its Application to Face Recognition
2-dimensional Projective Non-negative Matrix Factorization and Its Application to Face Recognition
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DOI：
10.3724/sp.j.1004.2012.01503
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Wei-tao Fang;Peng Ma;Zheng-Bin Cheng;Dan Yang;Xiaohong Zhang
中科院分区：
文献类型：
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作者：
Wei-tao Fang;Peng Ma;Zheng-Bin Cheng;Dan Yang;Xiaohong Zhang
通过最小化非负矩阵分解损失函数的人脸识别算法必须同时计算基矩阵和系数矩阵，导致计算复杂度较高。本文将非负性质引入二维主成分分析(2DPCA)，提出一种新颖的二维投影非负矩阵分解(2DPNMF)用于人脸识别。2DPNMF保留了人脸图像的局部结构，但突破了最小化非负矩阵损失函数的限制由于2DPNMF只需要计算投影矩阵（基矩阵），其计算复杂度大大降低。本文从理论上证明了该算法的收敛性，并利用YALE人脸数据库、FERET人脸数据库和AR人脸数据库进行了对比实验。实验结果表明，2DPNMF比NMF和2DPCA具有更高的识别性能和更快的速度。
Face recognition algorithms through minimizing the loss function of non-negative matrix factorization must simultaneously calculate the base matrix and the coe?cient 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 only 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.