中文摘要：

稀疏表示人脸识别算法在字典构造时易丢失大量分类信息且L1范数最小化计算量较大。针对此问题，提出一种基于Fisher准则字典学习和最小二乘法的压缩感知人脸识别算法。该算法首先由Fisher判别准则对训练样本训练得到字典；然后通过最小二乘法解L2范数最小化问题，得到人脸在该字典上的编码系数；最后结合各类别重构误差和编码系数对人脸分类。在公共人脸库上的测试结果表明，文中算法有较高的识别率，并有效提高识别速度。

英文摘要：

Sparse representation based classification （ SRC ） algorithm loses much discriminative information hidden in the training samples when constructing dictionary and the L1-minimization approach to solving the coding coefficient is computationally expensive. Aiming at these problems, a face recognition algorithm via compressive sensing based on Fisher discrimination dictionary learning and least square method is proposed. The training samples are trained by Fisher discrimination criterion and thus the structured dictionary is acquired. Then, the coding coefficients are obtained by solving L2-minimization problem through regularized least square method. Finally, the face is identified through the coding coefficient and reconstruction error. The experimental results clearly show that the proposed method has a better accuracy rate and improves the recognition speed compared with the existing sparse representation classification methods.