Identifying occluded faces has become a research hotpot for face image processing in recent years.Aiming at the problem of reconstruction of face occlusion area

a face completion algorithm based on conditional generative adversarial nets（ CGAN） is proposed. Firstly

the masking of human face is performed firstly by facial features extraction of facial features such as facial features through convolutional neural network（ CNN）

and it is used as a constraint information input generator and discriminator

in which the generator reconstructs occluded regions

reconstructs human faces and then inputs local regions respectively. The information discriminator and the global information discriminator combine the loss function to generate a complete human face. On the Celeb A dataset

comparing reconstructed and original faces

results shows that the algorithm can generate faces closer to the original image.