二维投影非负矩阵分解算法及其在人脸识别中的应用

方蔚涛; 马鹏; 成正斌; 杨丹; 张小洪

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

doi: 10.3724/SP.J.1004.2012.01503

1.
重庆大学计算机学院重庆 400030;
2.
重庆大学数学与统计学院重庆 401331;
3.
重庆大学软件学院重庆 401331

详细信息

通讯作者:
马鹏

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- 文章访问数: 2426
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- 被引次数: 0
出版历程
- 收稿日期: 2011-05-03
- 修回日期: 2012-03-19
- 刊出日期: 2012-09-20

2-dimensional Projective Non-negative Matrix Factorization and Its Application to Face Recognition

1.
College of Computer Science, Chongqing University, Chongqing 400030;
2.
College of Mathematics and Statistics, Chongqing University, Chongqing 401331;
3.
School of Software Engineering, Chongqing University, Chongqing 401331

摘要

摘要: 建立在最小化非负矩阵分解损失函数上的人脸识别算法需同时计算基矩阵和系数矩阵, 导致求解这类问题十分耗时. 本文把非负属性引入二维主成分分析(2-dimensional principal component analysis, 2DPCA)中, 提出了一种新的二维投影非负矩阵分解(2-dimensional projective non-negative matrix factorization, 2DPNMF)人脸识别算法. 该算法在保持人脸图像的局部结构情况下, 突破了最小化非负矩阵分解损失函数的约束, 仅需计算投影矩阵(基矩阵), 从而降低了计算复杂度. 本文从理论上证明了所提出算法的收敛性, 同时, 使用了YALE、FERET和AR三个人脸库进行实验, 结果表明2DPNMF不仅识别率高, 而且速度优于非负矩阵分解和二维主成分分析.
- 二维主成分分析 /
- 非负矩阵分解 /
- 人脸识别 /
- 特征提取
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 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.
- 2-dimensional principal component analysis (2DPCA) /
- non-negative matrix factorization (NMF) /
- face recognition /
- feature extraction

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参考文献(1)

[1]

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