SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis
This work is significant for applications requiring high-quality frontal face synthesis from challenging low-resolution and extreme-pose inputs, such as in surveillance or forensics, by improving identity preservation and detail reconstruction.
This paper addresses the challenge of synthesizing high-resolution, identity-preserving frontal faces from low-resolution, extreme-pose input faces. The proposed SuperFront-GAN (SF-GAN) model achieves this by integrating a super-resolution side-view module and an orthogonal constraint in the generator, demonstrating superior performance over existing methods.
Advances in face rotation, along with other face-based generative tasks, are more frequent as we advance further in topics of deep learning. Even as impressive milestones are achieved in synthesizing faces, the importance of preserving identity is needed in practice and should not be overlooked. Also, the difficulty should not be more for data with obscured faces, heavier poses, and lower quality. Existing methods tend to focus on samples with variation in pose, but with the assumption data is high in quality. We propose a generative adversarial network (GAN) -based model to generate high-quality, identity preserving frontal faces from one or multiple low-resolution (LR) faces with extreme poses. Specifically, we propose SuperFront-GAN (SF-GAN) to synthesize a high-resolution (HR), frontal face from one-to-many LR faces with various poses and with the identity-preserved. We integrate a super-resolution (SR) side-view module into SF-GAN to preserve identity information and fine details of the side-views in HR space, which helps model reconstruct high-frequency information of faces (i.e., periocular, nose, and mouth regions). Moreover, SF-GAN accepts multiple LR faces as input, and improves each added sample. We squeeze additional gain in performance with an orthogonal constraint in the generator to penalize redundant latent representations and, hence, diversify the learned features space. Quantitative and qualitative results demonstrate the superiority of SF-GAN over others.