Detailed Feature Guided Generative Adversarial Pose Reconstruction Network

Abstract

Face frontalization is a critical and difficult task on face pose reconstruction. Previous researches use simple posture information as guidance, such as pose coding and facial landmarks. To explore the guidance effect of profile faces, we propose detailed features that provide much detailed information. In this paper, a D etailed Feature G uided Generative Adversarial P ose R econstruction Network (DGPR) is proposed. Firstly, frontal pose coding and profile detailed features are fed into DGPR to generate detailed features of front face. Then, the second generator combines frontal detailed features and profile face to reconstruct front face. Besides, we propose a conditional enhancement loss to strengthen the guiding role of detailed features, and a smoothing loss to reduce edge sharpness in generated faces. Experimental results show that our method generates photorealistic front faces and outperforms state-of-the-art methods on M2FPA and CAS-PEAL. Specifically, DGPR improves the face recognition accuracy under pose angles of ±60°, ±75°, ±90° by 2%, 1%, and 6% respectively over the state-of-the-art methods on M2FPA, achieves the average rank-1 recognition rate to 99.95% and improves it by 0.05% on CAS-PEAL. These results demonstrate the effects of detailed features and corresponding modules.