Learning Unsupervised Face Normalization Through Frontal View Reconstruction

Face normalization from large pose is a challenging problem. Many Generative Adversarial Network (GAN) based models can infer frontal view of profile faces, while they require paired faces and pose label. Instead, we focus on frontal face synthesis with unpaired and unlabeled training data. We present a Frontal View Reconstruction based GAN (FVR-GAN) for large pose face normalization and recognition. The generator of FVR-GAN can be considered as a dual-input auto-encoder, where the identity encoder extracts identity features from an identity image and the template encoder extracts contour features from a frontal image. The decoder combines those two kinds of features and synthesizes a corresponding frontal face. To learn face normalization effectively, we incorporate the Frontal View Reconstruction (FVR) operation into training stage. The FVR operation includes self-reconstruction and frontalization mapping. A group of sub-discriminators which receive different facial parts are employed for discrimination. Considering that different face parts have different contributions to discrimination, we introduce a dynamic weighting mechanism to balance the output of sub-discriminators. FVR-GAN can recover high-quality frontal images under arbitrary poses. Experimental results on datasets of Multi-PIE, IJB-A, LFW and CFP demonstrate the efficacy of our model in terms of quality of synthesized images and recognition accuracy.