This paper addresses the problem of Face Recognition based on Image Set (FRIS) by kernel learning and proposed an extended kernel discriminant analysis framework for FRIS. By support vector machine learning, an image set from the original input space is mapped into the model space and described with Support Vector Domain Description (SVDD) to handle the underlying non-linearity in data space. In model space, a hyper-sphere encloses most of the mapped data, and the outliers lie outside the hyper-sphere. By exploring an efficient metric for the data domains in model space, we derive a kernel function maps the data from the model space to a high-dimensional feature space, to which many Euclidean algorithms can be generalized. The proposed method is evaluated on face recognition tasks. Comparisons with several state-of-the-art FRIS methods are performed on ChokePoint and CMU MoBo video database. The proposed methods have demonstrated promising performance. [ABSTRACT FROM AUTHOR]