Abstract: The paper described a general method for portrait synthesis using the generative adversarial network(GAN),which can generate a standard feature point aligned portrait image by cropping facial area from an in-the-wild photo.The main target of processed image is separated from background and the object detection and segmentation algorithm results are optimized.The processing pipeline includes two main parts:firstly,recognize head area using low-level hand-craft features;secondly,use the cropped area as the input of GAN to synthesis portrait image with facial feature aligned.This method can effectively extract facial parts of the image and avoid affection from the background pattern and objects,as well as enhance the facial segmentation of existing algorithms.The experimental results optimized the precision,recall and F-measure values of the existing segmentation algorithm,demonstrated in CelebA and LFW datasets,which are different from the self-made training dataset,and decreased the Facenet distance and standard deviation compared with the state-of-art face frontalization algorithms,showed well generalization ability and proved that this method can be widely used as preprocessing of image segmentation and portrait synthesis methods.