Massively Annotated Datasets for Assessment of Synthetic and Real Data in Face Recognition

arXiv:2404.15234v1 Announce Type: new
Abstract: Face recognition applications have grown in parallel with the size of datasets, complexity of deep learning models and computational power. However, while deep learning models evolve to become more capable and computational power keeps increasing, the datasets available are being retracted and removed from public access. Privacy and ethical concerns are relevant topics within these domains. Through generative artificial intelligence, researchers have put efforts into the development of completely synthetic datasets that can be used …

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