Enhancing Domain Diversity in Synthetic Data Face Recognition with Dataset Fusion
This addresses privacy concerns in face recognition by enhancing synthetic data diversity, but it is incremental as it builds on existing synthetic datasets.
The paper tackled the problem of synthetic data underperforming in face recognition due to model-specific artifacts from single generators, and found that fusing two architecturally distinct synthetic datasets reduces artifacts and improves performance on standard benchmarks.
While the accuracy of face recognition systems has improved significantly in recent years, the datasets used to train these models are often collected through web crawling without the explicit consent of users, raising ethical and privacy concerns. To address this, many recent approaches have explored the use of synthetic data for training face recognition models. However, these models typically underperform compared to those trained on real-world data. A common limitation is that a single generator model is often used to create the entire synthetic dataset, leading to model-specific artifacts that may cause overfitting to the generator's inherent biases and artifacts. In this work, we propose a solution by combining two state-of-the-art synthetic face datasets generated using architecturally distinct backbones. This fusion reduces model-specific artifacts, enhances diversity in pose, lighting, and demographics, and implicitly regularizes the face recognition model by emphasizing identity-relevant features. We evaluate the performance of models trained on this combined dataset using standard face recognition benchmarks and demonstrate that our approach achieves superior performance across many of these benchmarks.