This study proposes a multi-pose face recognition method with a two-cycle generative adversarial network to address low face recognition accuracy of non-frontal poses. The network consists of two aspects: face frontalization and face rotation. The face frontalization aspect converts profile faces to frontal faces and implements many-to-one pose category mapping. The face rotation aspect converts frontal faces to profile faces with specified poses, extracts the identity features of the frontal faces, and implements one-to-many pose category mapping. To further improve the face recognition of profile poses, two cyclic paths are used to combine the face frontalization and face rotation processes. One path is used for the cyclic conversion of profile faces to frontal faces and then to profile faces, and the other path is used for the cyclic conversion of frontal faces to profile faces and then to frontal faces. To reduce the difficulty in the training process and speed up the convergence of the network, the training process will be performed in two different stages: partial and complete training. Experiment results on Multi-PIE and CFP show that this method can effectively improve the recognition accuracy of profile poses.