Orthogonal laplacianfaces for face recognition
Orthogonal laplacianfaces for face recognition
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DOI：
10.1109/tip.2006.881945
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发表时间：
2006-11-01
影响因子：
10.6
通讯作者：
Zhang, Hong-Jiang
中科院分区：
文献类型：
--
作者：
Cai, Deng;He, Xiaofei;Zhang, Hong-Jiang
根据直觉，自然发生的人脸数据可以通过采样的概率分布上或附近的环境空间的子流形的支持，我们提出了一种基于外观的人脸识别方法，称为正交Laplacianface。我们的算法是基于局部保持投影（LPP）算法，其目的是找到一个线性逼近的特征函数的拉普拉斯Beltrami运营商的脸流形。然而，LPP是非正交的，这使得难以重构数据。正交局部保持投影（OLPP）方法产生正交基函数，并且可以具有比LPP更大的局部保持能力。由于局部保持能力潜在地与鉴别能力相关，所以OLPP被期望具有比LPP更高的鉴别能力。在三个人脸库上的实验结果表明了该算法的有效性。
Following the intuition that the naturally occurring face data may be generated by sampling a probability distribution that has support on or near a submanifold of ambient space, we propose an appearance-based face recognition method, called orthogonal Laplacianface. Our algorithm is based on the locality preserving projection (LPP) algorithm, which aims at finding a linear approximation to the eigenfunctions of the Laplace Beltrami operator on the face manifold. However, LPP is nonorthogonal, and this makes it difficult to reconstruct the data. The orthogonal locality preserving projection (OLPP) method produces orthogonal basis functions and can have more locality preserving power than LPP. Since the locality preserving power is potentially related to the discriminating power, the OLPP is expected to have more discriminating power than LPP. Experimental results on three face databases demonstrate the effectiveness or our proposed algorithm.