Aiming at the problem of low efficiency in obtaining training sample weights and solving the l₁ norm minimization, we proposed a face recognition algorithm WSRC_DALM algorithm, which was combined with Weighted Sparse Representation Classification (WSRC) and Dual Augmented Lagrangian Multiplier method (DALM). In the method, the Gaussian kernel function mainly was used to calculate the correlation between each training sample and the test sample, to obtain training samples with respect to the weight of the test sample. Then, the DALM algorithm was used to solve the l₁ norm minimization model, to achieve the test sample accurate reconstruction and classification. Finally, the proposed algorithm was validated by ORL and FEI datasets. In the ORL dataset, the recognition rate of the algorithm is 99%, compared with the classical SRC and WSRC algorithms, the recognition rate is improved by 7% and 4.8% respectively, and the computational efficiency is 20 times higher than WSRC algorithm. And in the FEI dataset, pose-varied face recognition rate is close to 92%. WSRC_DALM algorithm has obvious advantages in recognition accuracy and computational efficiency, and it has good robustness to large intraclass changes.