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Synthetic Data for Face Recognition: Bridging the Gap Between Real and Synthetic Datasets
The core message of this article is to present the Synthetic Data for Face Recognition (SDFR) competition, which was organized to accelerate research in synthetic data generation for privacy-friendly face recognition models and to bridge the gap between real and synthetic face datasets.
Enhancing Face Recognition Accuracy in Real-World Conditions with a Dual-Input Adapter Framework
An effective adapter framework that processes both low-quality and high-quality facial images to bridge the domain gap and enhance face recognition accuracy in real-world scenarios.
Leveraging Synthetic Faces to Reduce Authentic Data Demand in Face Recognition
Synthetic face data can be effectively combined with limited authentic data to train accurate face recognition models, reducing the reliance on large-scale authentic datasets.
Face Verification Explanation Using Gradient Backpropagation
Face verification explanation through Feature-Guided Gradient Backpropagation.
