Lu Jiwen, Tan Yap-Peng
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798.
IEEE Trans Syst Man Cybern B Cybern. 2010 Jun;40(3):958-63. doi: 10.1109/TSMCB.2009.2032926. Epub 2009 Nov 10.
We propose in this paper a parametric regularized locality preserving projections (LPP) method for face recognition. Our objective is to regulate the LPP space in a parametric manner and extract useful discriminant information from the whole feature space rather than a reduced projection subspace of principal component analysis. This results in better locality preserving power and higher recognition accuracy than the original LPP method. Moreover, the proposed regularization method can easily be extended to other manifold learning algorithms and to effectively address the small sample size problem. Experimental results on two widely used face databases demonstrate the efficacy of the proposed method.
我们在本文中提出一种用于人脸识别的参数化正则化局部保持投影（LPP）方法。我们的目标是以参数化方式调节LPP空间，并从整个特征空间而非主成分分析的降维投影子空间中提取有用的判别信息。这使得该方法比原始LPP方法具有更好的局部保持能力和更高的识别准确率。此外，所提出的正则化方法可以很容易地扩展到其他流形学习算法，并有效解决小样本问题。在两个广泛使用的人脸数据库上的实验结果证明了该方法的有效性。
