Under the unconstrained conditions, intra-personal variation is much larger than the inter-personal variation in face images due to the affecting factors such as expression, posture, illumination and background etc. To reduce the influence of larger intra-personal on face verification, we proposed a similarity metric learning method with priori similarity and priori distance constraint by combining weighted subspace.First, the weighted intra-personal covariance matrix is learned by employing intra-personal face samples. By projecting into the intra-subspace, robust face feature representations can be obtained from face images.Second, we set up the similarity metric learning model with priori similarity and priori distance constraint, which effectively employs the similarity and discrimination information of samples that are in pairs, and the learned metric matrix can improve the robustness to intra-personal and discrimination to inter-personal.Finally, the updated metric matrix is used to compute the similarity scores of face-pairs. The experiments have been conducted on the Labeled Faces in the Wild (LFW) dataset, which shows the effectiveness of our proposed model. Compared with other metric learning methods, our learned metric matrix has higher accuracy rate for evaluating the face-pair similarity, and achieves a verification rate of 91.2% on the restricted setting.