非负二维主成分分析及在人脸识别中的应用

二维主成分分析是一种基于整体脸的方法,保留人脸部件之间的拓扑关系.而非负矩阵分析是基于局部特征的识别,是通过提取局部信息来实现分类.文中将两种思想的优点融合在一起,提出非负二维主成分分析.该方法改善传统非负矩阵分解只是从矩阵分解的角度考虑,没有加强分类的问题.此外,该方法在矩阵分解之前不需要将图像矩阵转换为图像向量,能快速降低鉴别特征的维数.在ORL和FERET人脸库上的实验结果表明,该方法在识别性能上优于其它方法,且更具有鲁棒性.

Non-Negative Two-Dimensional Principal Component Analysis and Its Application to Face Recognition

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.