Face recognition has received substantial attention for a long time. Many typical methods have been proposed to perform face recognition.1–4 Since Wright et al. presented the sparse representation-based classification (SRC) method,5 it has been widely studied in many pattern recognition applications due to its promising results, such as face recognition,6,7 along with gender,8 digit,9,10 biology data,11,12 and medical image13,14 classification. Although many improved SRC-based methods have been proposed for robust face recognition,15–19 most of them require rigid image alignment, where all images of an object or objects of interest are aligned to a fixed canonical template. Until now, much work has been performed to address the alignment problem.20,21 However, such alignment is still difficult to achieve in real scenarios, such as partial face, scale, or pose variation face recognition. To address the alignment problem in SRC, some methods22,23,24 introduced the scale-invariant feature transform (SIFT)22 or the speeded-up robust features25 descriptor to the recognition method. However, most of these methods pay little attention to the correlation among the query descriptors, which is found to be useful for classification. Thus, it is necessary to study a method exploiting the correlation of the query descriptors for robust alignment-free face recognition, which is the focus of this paper.