SmoothFace: Class-Conditional Label Smoothing for Synthetic-based Face Recognition

The major improvements in face recognition (FR) in recent years have been supported by large face databases. However, there are concerns about the legal and ethical aspects of using large authentic databases with the proper consent from individuals being questioned. Motivated by this, and by the technical need for larger and more diverse data, synthetic datasets are being increasingly used, taking advantage of recent advances in the field of generative models. A major challenge there is ensuring the generation of synthetic face images with realistic and controllable class separability. In this paper, we aim to enhance class separability, which is commonly low in GAN-based synthetic FR data and affects synthetic based FR performance. To achieve that, we propose a novel label smoothing scheme within a class-conditional generation process. The smoothing aims at going beyond hard labels that induce a class label to the generation, by pushing the generation process away from other classes. In extensive experiments, we show the benefit of label smoothing in the generative setup by showing increased class separability. This is also reflected in the models trained on the proposed data by outperforming its hard label baseline and the state-of-the-art GAN-based synthetic-based FR approaches on multiple established verification benchmarks.