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Image set representation and classification is an important problem in computer vision and pattern recognition area. It has been widely used in many computer vision applications. In this paper, a new image set representation and classification method has been proposed. The main contributions of this paper are twofold: (1) a new image set representation model, called attributed covariate-relation graph (ACRG), has been proposed for image set representation and modeling. ACRG aims to represent image set with an attributed graph model which involves both image features and their spatial structure simultaneously. (2) A new graph data based sparse representation and classification method, called Graph Sparse Representation Classification (GSRC) has been proposed to achieve ACRG classification. Experimental results on several datasets demonstrate the benefits of the proposed ACRG representation and GSRC classification. [ABSTRACT FROM AUTHOR]