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.