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Found 3 papers, 2 papers with code
Bias and Diversity in Synthetic-based Face Recognition
In this work, we investigate how the diversity of synthetic face recognition datasets compares to authentic datasets, and how the distribution of the training data of the generative models affects the distribution of the synthetic data.
Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation
Explainable Face Recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications.
SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data
The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries.
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