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Due to the shortcomings of the traditional SRC in face recognition, a face recognition method with one training image per person has been proposed, and it is based on compressed sensing. We apply three level cascades virtual sample method to generate multiple samples of each person. These generated samples have multi-expressions and multi-gestures are added to the original sample set for training. Then, a super sparse random projection and weighted optimization are applied to improve the SRC. This proposed method is named weighted super sparse representation classification (WSSRC) and is used for face recognition in this paper. In the case of the single sample,,experiments on the well-known ORL face dataset show that WSSRC is about 15.53% more accurate than the original SRC method and on the FERET face dataset,it is increased by 7.67%.In addition, compared to RSRC ,SSRC,DMMA,DCT-based DMMA and I-DMMA, WSSRC also achieve higher recognition rates .