基于生成对抗网络的偏转人脸转正

为了提高偏转人脸转正的效果，借鉴双通道生成对抗网络（TP-GAN）双通道生成的思想，将原始网络中的深度卷积生成对抗网络（DCGAN）替换成边界均衡生成对抗网络（BEGAN）. 在传统两者对抗的网络结构中加入判别人脸身份的分类器，形成三者对抗的网络结构. 经实验对比可知，与在生成器损失函数中添加约束相比，结构上加入分类器对人脸身份一致性的保持更加有效. TP-GAN存在训练复杂、模式崩溃等难题，使用BEGAN的网络结构，可以避免这些问题，提高训练效率. 在Multi-PIE数据集及LFW上的实验结果表明，利用提出的方法能够高效地生成高质量的正面人脸图片，且保留人脸的身份特征.

Face frontalization based on generative adversarial network

The original deep convolutional generative adversarial network (DCGAN) structure was replaced with boundary equilibrium generative adversarial network (BEGAN) by drawing on the experience of two-pathway generative adversarial network (TP-GAN) in order to improve the effect of frontalizing the rotated faces. The classifier used for discriminating face identity was added to the traditional two player network in order to form the three-player structure. Adding classifier is more effective for maintaining the consistency of face identity compared with adding constraints in the generator loss function. The adopted BEGAN can improve the training efficiency aiming at the problem that TP-GAN are complex to train and prone to the mode collapse. The experimental results on the multiple images across pose, illumination and expression (Multi-PIE) and labeled faces in the wild (LFW) dataset show that the proposed method can generate high-quality frontal face images with retaining the identity information.