Synthetic to Authentic: Transferring Realism to 3D Face Renderings for Boosting Face Recognition
This addresses the challenge of collecting large real face datasets for training face recognition systems, offering a pathway to use synthetic data more effectively, though it is incremental as it builds on existing image-to-image translation techniques.
The paper tackles the problem of face recognition systems underperforming when trained on synthetic 3D-rendered facial images compared to real datasets, and shows that using image-to-image translation to make these synthetic images look more realistic boosts performance on real-world benchmarks.
In this paper, we investigate the potential of image-to-image translation (I2I) techniques for transferring realism to 3D-rendered facial images in the context of Face Recognition (FR) systems. The primary motivation for using 3D-rendered facial images lies in their ability to circumvent the challenges associated with collecting large real face datasets for training FR systems. These images are generated entirely by 3D rendering engines, facilitating the generation of synthetic identities. However, it has been observed that FR systems trained on such synthetic datasets underperform when compared to those trained on real datasets, on various FR benchmarks. In this work, we demonstrate that by transferring the realism to 3D-rendered images (i.e., making the 3D-rendered images look more real), we can boost the performance of FR systems trained on these more photorealistic images. This improvement is evident when these systems are evaluated against FR benchmarks utilizing real-world data, thereby paving new pathways for employing synthetic data in real-world applications.