Kernel Dual Linear Regression for Face Image Set Classification

Abstract

DLRC is an extension of LRC that extends LRC from conventional still image based method to the image set based method. DLRC has a demonstrated better performance on image set classification. However, when the image sets of different objects are not linear separable, or when the linear regression axes of class-specific samples of different classes have an intersection, DLRC may be failed for well classifying the image sets. In this paper, a new classification method, kernel dual linear regression classification (KDLRC), is proposed. KDLRC is a nonlinear version of DLRC and can overcome the drawback of DLRC. KDLRC first embeds the input data into a high-dimensional Hilbert space, then in the kernel space, the data become easier to classify. Extensive experiments on four well-known databases prove that the performance of KDLRC is better than that of DLRC and several state-of-the-art classifiers.