In order to solve the low efficiency problem of the combined twodimensional principal component analysis (2DPCA) and principal component analysis (PCA) in extracting face features, we propose a face recognition method based on the support vector machine optimized by the 2DPCA and fast PCA combined with an improved gray wolf algorithm (EGWO). Concerning the feature extraction, the method combines the 2DPCA with fast PCA to reduce the dimension and extraction time of the extracted features, thus reducing the identification time required by the SVM. In order to improve the global search capability of the gray wolf algorithm, the elite opposite-based learning strategy is used to initialize population individuals, which effectively enhances GWO’s exploration and mining capabilities. And then the elite opposite-based learning strategy is used in the SVM to iteratively obtain the best kernel parameters and disciplinary parameters, and the final classifier obtained from training is applied in face recognition. The GWO and opposite-based learning grey wolf algorithm (OGWO) are compared on six benchmark test functions in convergence accuracy and speed, and the former outperforms the latter. Experiments on face images in ORL and Yale datasets show that the improved algorithm is better and more stable than the GWO, particle swarm optimization (PSO) and differential evolution (DE) algorithm combined with SVM model.