Concerning the problem that single face image is difficult to solve the large-pose profile face in face reconstruction, a face reconstruction method based on Multi-pose Feature Fusion Generative Adversarial Network (MFFGAN) was proposed. In this method, the relevant information between multiple profile faces with different poses was used for face reconstruction, and the adversarial mechanism was used to adjust network parameters. A new network was designed in the method, which consisted of a generator including multi-pose feature extraction, multi-pose feature fusion and frontal face synthesis, and a discriminator for adversarial training. In the multi-pose feature extraction module, multiple convolution layers were used to extract the multi-pose features of profile face images. In the multi-pose feature fusion module, the multi-pose features were fused into a fusion feature containing multi-pose face information. And, the fusion feature was added during the face reconstruction process in the frontal face synthesis module. Obtaining the relevant information and global structure by exploring the feature dependency between multi-pose profile face images can effectively improve the reconstruction results. Experimental results show that, compared with those of the state-of-the-art deep learning based face reconstruction methods, the contours of the frontal face recovered by the proposed method are clear, and the recognition rate of the frontal face recovered from two profile faces is increased by 1.9 percentage points on average; and the more profile faces are input, the higher the recognition rate of the recovered frontal face is, which indicates that the proposed method can effectively fuse multi-pose features to recover a clear frontal face.