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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

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. 自动化学报, 2012, 38(9): 1503-1512. doi: 10.3724/SP.J.1004.2012.01503
引用本文: 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. 自动化学报, 2012, 38(9): 1503-1512. doi: 10.3724/SP.J.1004.2012.01503
FANG Wei-Tao, MA Peng, CHENG Zheng-Bin, YANG Dan, ZHANG Xiao-Hong. 2-dimensional Projective Non-negative Matrix Factorization and Its Application to Face Recognition. ACTA AUTOMATICA SINICA, 2012, 38(9): 1503-1512. doi: 10.3724/SP.J.1004.2012.01503
Citation: FANG Wei-Tao, MA Peng, CHENG Zheng-Bin, YANG Dan, ZHANG Xiao-Hong. 2-dimensional Projective Non-negative Matrix Factorization and Its Application to Face Recognition. ACTA AUTOMATICA SINICA, 2012, 38(9): 1503-1512. 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
详细信息
    通讯作者:

    马鹏

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

  • 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.
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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

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