Face recognition using Laplacianfaces
Face recognition using Laplacianfaces
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DOI：
10.1109/tpami.2005.55
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发表时间：
2005-03-01
影响因子：
23.6
通讯作者：
Zhang, HJ
中科院分区：
文献类型：
--
作者：
He, XF;Yan, SC;Zhang, HJ
我们提出了一种基于外观的人脸识别方法称为Laplacianface方法。通过局部保持投影（LPP），人脸图像映射到一个人脸子空间进行分析。与主成分分析（PCA）和线性判别分析（LDA）只能有效地看到人脸空间的欧氏结构不同，LPP找到了一个保留局部信息的嵌入，并获得了一个最好地检测基本人脸流形结构的人脸子空间。Laplacian面是面流形上的拉普拉斯Beltrami算子的特征函数的最佳线性逼近。以这种方式，可以消除或减少由照明、面部表情和姿势的变化引起的不想要的变化。理论分析表明，PCA，LDA和LPP可以从不同的图模型。我们比较了三个不同的人脸数据集上的特征脸和Fisherface方法提出的Laplacianface方法。实验结果表明，所提出的Laplacianface方法提供了一个更好的表示，并在人脸识别中实现了较低的错误率。
We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.