With the development of face image syn-thesis and generation technology based on generative ad-versarial networks(GANs),it has become a research hot-spot to determine whether a given face image is natural or generated.However,the generalization capability of the existing algorithms is still to be improved.Therefore,this paper proposes a general algorithm.To do so,firstly,the learning on important local areas,containing many face key-points,is strengthened by combining the global and local features.Secondly,metric learning based on the ArcFace loss is applied to extract common and discrimin-ative features.Finally,the extracted features are fed into the classification module to detect GAN-generated faces.The experiments are conducted on two publicly available natural datasets(CelebA and FFHQ)and seven GAN-generated datasets.Experimental results demonstrate that the proposed algorithm achieves a better generalization performance with an average detection accuracy over 0.99 than the state-of-the-art algorithms.Moreover,the pro-posed algorithm is robust against additional attacks,such as Gaussian blur,and Gaussian noise addition.