为了使得稀疏表示分类方法具有更好的识别效果，提出了基于单演特征的稀疏表示分类( MSRC) 方法． 相对于Gabor 特征，单演特征能够用于提取图像的相位信息，而相位信息对光照不敏感，因此MSRC 方法能提高图像的光照鲁棒性． 相对于Gabor 特征的多尺度和多方向，单演特征能够减少特征的处理时间． 实验结果表明: 文中所提的方法具有使用价值，识别率和速度方面得到了一定的提升．

In order to promote the recognition effect of the sparse representation classification method,a monogenicfeature-based sparse representation classification ( MSRC) method is proposed.As compared with the Gabor feature,the monogenic feature can be used to extract the image phase information which is not sensitive to light,so theMSRC method can improve the image robustness to light.As compared with the multi-scale and multi-direction ofthe Gabor feature,the monogenic feature can reduce the processing time spent on the feature.Experimental resultsshow the proposed method is applicable with higher recognition rate and speed.