Abstract Two-dimensional principal component analysis (2DPCA) is an algorithm based on the whole face and it preserves the topology of facial components. Non-negative matrix factorization (NMF) is an algorithm based on localized features and extracts local information. A method for human face recognition is proposed, namely, non-negative 2-dimensional principal component analysis (N2DPCA). N2DPCA integrates the merits of 2DPCA and NMF. And it can overcome the demerits of traditional NMF. Furthermore, the proposed method does not require transformation from a 2D image matrix into a 1D long vector. The experimental results on ORL and FERET face database show that the proposed method achieves higher recognition rate and stronger robustness than 2DPCA, NMF and LNMF.
YAN Hui,JIN Zhong,YANG Jing-Yu. Non-Negative Two-Dimensional Principal Component Analysis and Its Application to Face Recognition[J]. , 2009, 22(6): 809-814.
[1] Liu Ke, Cheng Yongqing, Yang Jingyu, et al. Algebraic Feature Extraction for Image Recognition Based on an Optimal Discriminant Criterion. Pattern Recognition, 1993, 26(6): 903-911 [2] Yang Jian, Zhang D, Frangi A F, et al. Two Dimensional PCA:A New Approach to Appearance Based Face Representation and Recognition. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137 [3] Lee D D, Seung H S. Learning the Parts of Objects with Non-Negative Matrix Factorization. Nature, 1999, 401: 788-791 [4] Li S Z, Hou Xinwen, Zhang Hongjiang, et al. Learning Spatially Localized, Parts-Based Representation // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai Marriott, USA, 2001,Ⅰ: 207-212 [5] Hoyer P O. Non-Negative Sparse Coding // Proc of the IEEE Workshop on Neural Networks for Signal Processing. Martigny, Switzerland, 2002: 557-565 [6] Olshausen B A, Field D J. Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images. Nature, 1996, 381: 607-609 [7] Liu Weixiang, Zheng Nanning, Lu Xiaofeng. Non-Negative Matrix Factorization for Visual Coding // Proc of the IEEE International Conference on Acoustics, Speech and Signal Processing. Hongkong, China, 2003, Ⅲ: 293-296 [8] Zafeiriou S, Tefas A, Pitas I. Discriminant NMF Faces for Frontal Face Verification // Proc of the IEEE International Workshop on Machine Learning for Signal Processing. Mystic, USA, 2005: 355-359 [9] Paatero P, Tapper U. Positive Matrix Factorization: A Non-Negative Factor Model with Optimal Utilization of Error Estimates of Data Values. Environmetrics, 1994, 5(2): 111-126 [10] Liu Weixiang. Non-Negative Matrix Factorization and Its Application. Xi'an, China: Xi'an Jiaotong University Press, 2005 (in Chinese) (刘维湘.非负矩阵分解及其应用.西安:西安交通大学出版社, 2005) [11] Lee D D, Seung H S. Algorithms for Non-Negative Matrix Factorization // Leen T K, Dietterich T G, Tresp V. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2002, 13: 556-562 [12]Penev P S, Atick J J, Local Feature Analysis: A General Statistical Theory for Object Representation. Neural System, 1996, 7(3): 477-500 [13] Feng Tao, Li S Z, Shum H, et al. Local Non-Negative Matrix Factorization as a Visual Representation // Proc of 2nd International Conference on Development and Learning. Washington, USA, 2002: 178-183 [14] Turk M, Pentland A. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 71-86
2009, Vol. 22 
