Face Recognition Using Kernel Ridge Regression

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 of face 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, Fisherfaces and Laplacianfaces...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Senjian An, Wanquan Liu, Svetha Venkatesh
• Keywords: Computer Vision, CVPR 2007, Face Databases, Face Images, Face Recognition Techniques, Face Subspace, Training Face