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.
严慧，金忠，杨静宇. 非负二维主成分分析及在人脸识别中的应用*[J]. 模式识别与人工智能, 2009, 22(6): 809-814.
YAN Hui, JIN Zhong, YANG Jing-Yu. Non-Negative Two-Dimensional Principal Component Analysis and Its Application to Face Recognition. , 2009, 22(6): 809-814.
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2009, Vol. 22 
