Abstract: The set-to-set distance based methods ignores the relationship between gallery sets, while representing the query set images individually over the gallery sets ignores the correlation between query set images. In view of multiple representations of images contributing to providing complementary information, hull of image set based collaborative representation for face recognition is proposed. Firstly, the extended image set with multiple representations is structured. Due to the images with pixels with moderate intensities of the original images carry discriminatory information and the mirror images can somewhat overcome the misalignment problem of the face image in face recognition， the extended image set can be obtained by jointing the domain images of the original images and mirror images and pixels with moderate intensities images. Secondly, the extended dictionary is modeled as hull dictionary with non-parametric approaches for image set modeling and the query set from the same class of different domain image sets is modeled as a hull. The idea of collaborative representation and iterations are used to solve the coefficient of hull. Finally, the query set is classified by using of residual error SRC criterion. This method not only structures the image set with multiple representations contributing to the accuracy of image classification, but also makes full use of the relationship between image sets. Experimental results verify the proposed algorithm effectiveness respectively in ORL、GT（Georgia Tech Face Database）、CMU PIE facial database.