Parts-Based Holistic Face Recognition with RBF Neural Networks
Parts-Based Holistic Face Recognition with RBF Neural Networks
复制标题
DOI：
10.1007/11760023_17
复制
发表时间：
2006-05
期刊：
影响因子：
--
通讯作者：
Wei Zhou-;X. Pu;Ziming Zheng
中科院分区：
文献类型：
--
作者：
Wei Zhou-;X. Pu;Ziming Zheng
提出了一种结合稀疏约束非负矩阵分解和径向基函数分类器的人脸识别方法。NMF可以通过约束基图像的稀疏性来表示基于局部或整体特征的面部图像。在低稀疏度和高稀疏度的NMF和主成分分析（PCA）方法对有遮挡和无遮挡人脸的识别进行了对比实验。仿真结果表明，在识别有遮挡的人脸时，RBF分类器的性能明显优于最近邻线性分类器，且整体表示对遮挡或噪声的敏感性低于局部表示。
This paper proposes a method for face recognition by integrating non-negative matrix factorization with sparseness constraints (NMFs) and radial basis function (RBF) classifier. NMFs can represent a facial image based on either local or holistic features by constraining the sparseness of the basis images. The comparative experiments are carried out between NMFs with low or high sparseness and principle component analysis (PCA) for recognizing faces with or without occlusions. The simulation results show that RBF classifier outperformsk–nearest neighbor linear classifier significantly in recognizing faces with occlusions, and the holistic representations are generally less sensitive to occlusions or noise than parts-based representations.