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Two-Step Data Augmentation for Masked Face Detection and Recognition: Turning Fake Masks to Real
The absence of large-scale masked face datasets challenges 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 via GANs, producing masked face samples that go beyond rule-based
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