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Synthetic Data for the Mitigation of Demographic Biases in Face Recognition
They primarily arise from the unequal representations of demographic groups in the training data.
GANDiffFace: Controllable Generation of Synthetic Datasets for Face Recognition with Realistic Variations
We generate multiple synthetic datasets by changing GANDiffFace settings, and compare their mated and non-mated score distributions with the distributions provided by popular real-world datasets for face recognition, i. e. VGG2 and IJB-C. Our results show the feasibility of the proposed GANDiffFace, in particular the use of Diffusion models to enhance the (limited) intra-class variations provided by GANs towards the level of real-world datasets.
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