Efficient Practices for Profile-to-Frontal Face Synthesis and Recognition

Abstract

Despite the great progress of deep learning and generative adversarial networks, face frontalization (i.e., profile-to-frontal synthesis) and profile (i.e., non-frontal) face recognition still remain challenging tasks under uncontrolled environments. In this study, we propose three efficient practices to improve the performance of profile-to-frontal face synthesis and recognition. Firstly, the identity preserving module is embedded to constrain synthesized frontal images similar to true frontal faces in feature space. Secondly, facial consistency loss is employed to reduce the artifact of the generated frontal face in pixel space. Lastly, the multi-model embedded scheme enhances the representation learning through diverse facial features extracted by multiple facial feature extractors. The proposed practices are general, though specifically deployed to CR-GAN in this study for performance verification. Experimental results on Multi-PIE and VGGFace2 demonstrate that the proposed practices qualitatively generate more realistic photography frontal faces and quantitatively obtain better face recognition accuracy.