Face Recognition Using Kernel Ridge Regression
Face Recognition Using Kernel Ridge Regression
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DOI：
10.1109/cvpr.2007.383105
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发表时间：
2007-06
期刊：
影响因子：
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通讯作者：
S. An;Wanquan Liu;S. Venkatesh
中科院分区：
文献类型：
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作者：
S. An;Wanquan Liu;S. Venkatesh
在本文中，我们提出了新的岭回归（RR）和核岭回归（KRR）技术的多元标签和应用的方法的问题擦除识别。由于正则单形顶点是具有最高对称度的独立点，我们选择这些顶点作为识别中不同个体的目标，并应用RR或KRR将训练人脸图像映射到人脸子空间中，每个个体的训练图像将位于其各自目标附近。我们通过将新的人脸图像映射到这个人脸子空间并比较其与所有单个目标的距离来识别新的人脸图像。还提供了一种有效的交叉验证算法来选择正则化和核参数。在两个人脸数据库上进行的实验表明，该算法的识别性能明显优于三种常用的线性人脸识别技术（Eigenfaces、Fisher faces和Laplacian faces），并且与最近发展的正交Laplacian faces相比，具有运算速度快的优点。实验结果也表明，由于KRR可以利用人脸图像的非线性结构，因此KRR的性能优于RR。虽然我们集中在人脸识别在本文中，所提出的方法是通用的，可以适用于一般的多类别分类问题。
In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques for multivariate labels and apply the methods to the problem efface recognition. Motivated by the fact that the regular simplex vertices are separate points with highest degree of symmetry, we choose such vertices as the targets for the distinct individuals in recognition and apply RR or KRR to map the training face images into a face subspace where the training images from each individual will locate near their individual targets. We identify the new face image by mapping it into this face subspace and comparing its distance to all individual targets. An efficient cross-validation algorithm is also provided for selecting the regularization and kernel parameters. Experiments were conducted on two face databases and the results demonstrate that the proposed algorithm significantly outperforms the three popular linear face recognition techniques (Eigenfaces, Fisher faces and Laplacian faces) and also performs comparably with the recently developed Orthogonal Laplacian faces with the advantage of computational speed. Experimental results also demonstrate that KRR outperforms RR as expected since KRR can utilize the nonlinear structure of the face images. Although we concentrate on face recognition in this paper, the proposed method is general and may be applied for general multi-category classification problems.