针对稀疏描述方法因计算复杂度高难以满足实际需求以及对训练样本个数敏感的问题，提出一种差异性 Shearlet 特征的快速稀疏描述人脸识别方法。首先对人脸图像采用 Shearlet变换得到多尺度多方向的人脸特征；然后利用匹配得分融合策略对 Shearlet特征进行融合，构成差异性特征；进而构造针对每个测试样本的“最佳”稀疏表示并计算其相关系数；最后依据训练样本在描述测试样本中所做贡献的大小，实现对测试样本图像的分类识别。在 ORL和 YALE人脸库上的实验结果表明，所提算法在保证高识别率优势的同时大大降低了时间复杂度。

For the high computational complexity of sparse description which results in the difficulty in meeting the actual needs and the number of samples sensitive training, we proposed a differences Shearlet characteristic of fast sparse description method to describe face recognition. First, Shearlet was used to get multi-scale and multi-direction facial image. Then a matching score fusion strategy was used to integrate Shearlet characteristics, and discriminative characteristics were constituted. Furthermore, configured the "best" sparse description for each test sample and calculated the correlated coefficient. Finally, according to the contribution size of the training sample in a test sample description, achieved the test sample image classification. Experimental results on ORL and YALE face database show that the algorithm ensuring a high recognition rate advantage as well as significantly reducing the time complexity.