Two-Step Data Augmentation for Masked Face Detection and Recognition: Turning Fake Masks to Real

Excerpt

arXiv:2512.15774v5 Announce Type: replace-cross Abstract: 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 overlays. Trained on about 19,100 images in the target domain (3.8% of IAMGAN's scale), or, including out-of-domain transfer pretraining, 59,6