Mitigating the Impact of Attribute Editing on Face Recognition
arxiv(2024)
摘要
Facial attribute editing using generative models can impair automated face
recognition. This degradation persists even with recent identity-preserving
models such as InstantID. To mitigate this issue, we propose two techniques
that perform local and global attribute editing. Local editing operates on the
finer details via a regularization-free method based on ControlNet conditioned
on depth maps and auxiliary semantic segmentation masks. Global editing
operates on coarser details via a regularization-based method guided by custom
loss and regularization set. In this work, we empirically ablate twenty-six
facial semantic, demographic and expression-based attributes altered using
state-of-the-art generative models and evaluate them using ArcFace and AdaFace
matchers on CelebA, CelebAMaskHQ and LFW datasets. Finally, we use LLaVA, a
vision-language framework for attribute prediction to validate our editing
techniques. Our methods outperform SoTA (BLIP, InstantID) at facial editing
while retaining identity.
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