Progressive Pose Normalization Generative Adversarial Network For Frontal Face Synthesis And Application In Face Recognition

Nowadays many deep learning-based face recognition algorithms have been widely applied in real life,but most of these face recognition algorithms require that the face to be recognized has a more positive posture.If the posture of the face is relatively lateral or it is one of the entire side face,it will lead to a greatly increased error rate in face recognition,and even cause the crash of the face recognition system.Therefore,the problem that the head posture affects the normal operation of the face recognition system has become an urgent problem.Many researchers at home and abroad have also begun to study the impact of poses on face recognition gradually.The solutions of the problem are divided into two categories,one is the learning of a convolutional neural network that is robust to poses,and the other is a process of face normalization by generating adversarial networks.The method proposed in this paper is a face synthesis algorithm for Progressive Pose-Normalization Generative Adversarial Network(PPN-GAN).Compared with other synthesis algorithms,the image synthesized by this algorithm has higher quality,and the face recognition after synthesis also has higher accuracy.The other synthesis algorithms are one-step methods which regardless of the angle of the profile image.By designing different generative adversarial networks and loss functions,these profile images are directly synthesized into the frontal view image.On the contrary,the algorithm proposed in this paper is a multi-step method.Through a progressive generation strategy,profile images are gradually synthesized into frontal faces.The problem of synthesizing a profile image into a frontal image itself is a morbid problem,and also an inverse problem.Because too much information is lost during the synthesis of a profile face to the frontal face,it is difficult to synthesize the frontal face directly from the profile face.Meanwhile,synthesis through one-step method makes the model difficult to train and the quality of the synthesized image is low.Progressive Pose-Normalization Generative Adversarial Network for frontal face synthesis transits by searching for multiple intermediate faces.We gradually transform the profile face to the middle face,and then transform from the middle face to the next middle face,until it finally returns to a positive face.In addition to the progressive strategy,an additional identity discriminator and identity aware losses in both the image and feature spaces are also incorporated into the GAN for identity preserving.This progressive strategy improves the performance of frontal face synthesis mainly in two aspects:(1)The amount of uncertain information is decomposed,so that the learning burden faced by GAN is reduced.(2)More prior knowledge from data can be explored during synthesizing the intermediate views of a face,which is helpful for recovering the uncertain information.The main contributions of this Method are threefold:(1)We propose a progressive pose-normalization framework based on GAN for frontal face synthesis,which can recover photorealistic and identity-preserving frontal faces from face images under extremely large pose.(2)We incorporate an additional identity discriminator and identity-aware losses for both the image and feature spaces into the GAN architecture,which helps faithfully preserve identities.(3)We introduce a sample-specific "pose router" to direct different testing samples into corresponding stages of the PPN-GAN,so that optimized frontal faces can be synthesized by the PPN-GAN.Experimental results show that our method not only produces compelling perceptual results but also outperforms the state-of-the-art methods on face recognition under large-pose.