Indirect Immunofluorescence (IIF) on Human Epithelial-2 (HEp-2) cells is the hallmark method for detecting some specific autoimmune diseases by identifying the presence of antinuclear antibodies (ANAs) within a patient's serum. Due to the limitations of IIF, such as being subjective and time consuming, automated Computer-aided diagnosis (CAD) system is required for diagnostic purposes. In this paper, we propose a novel feature extraction scheme for automatic staining pattern classification of HEp-2 cells. Our method constructs a dual spatial pyramid structure on a powerful rotation invariant texture feature, which has the following advantages: (1) invariance under local rotation of the image, (2) robustness against resolution changes, and (3) strong descriptive ability. Incorporated with a linear SVM classifier, our approach demonstrates its effectiveness by testing on two HEp-2 cells datasets: the ICPR2012 dataset and the ICIP2013 training dataset. Particularly, it shows superior classification performance than the best performer at the first edition of the HEp-2 cell classification contest.