AI RESEARCH
Two-Step Data Augmentation for Masked Face Detection and Recognition: Turning Fake Masks to Real
arXiv CS.LG
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ArXi:2512.15774v3 Announce Type: replace-cross The absence of large-scale masked face datasets poses challenges for masked face detection and recognition. We propose a two-step generative data augmentation framework combining rule-based mask warping with unpaired image-to-image translation using GANs, producing masked face samples that go beyond rule-based geometric overlays. Trained on 3390 images, about 0.7% of the