On Generating Identifiable Virtual Faces
- URL: http://arxiv.org/abs/2110.07986v1
- Date: Fri, 15 Oct 2021 10:19:48 GMT
- Title: On Generating Identifiable Virtual Faces
- Authors: Zhuowen Yuan, Sheng Li, Xinpeng Zhang, Zhenxin Qian, Alex Kot
- Abstract summary: Face anonymization with generative models have become increasingly prevalent since they sanitize private information.
In this paper, we formalize and tackle the problem of generating identifiable virtual face images.
We propose an Identifiable Virtual Face Generator (IVFG) to generate the virtual face images.
- Score: 13.920942815539256
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Face anonymization with generative models have become increasingly prevalent
since they sanitize private information by generating virtual face images,
ensuring both privacy and image utility. Such virtual face images are usually
not identifiable after the removal or protection of the original identity. In
this paper, we formalize and tackle the problem of generating identifiable
virtual face images. Our virtual face images are visually different from the
original ones for privacy protection. In addition, they are bound with new
virtual identities, which can be directly used for face recognition. We propose
an Identifiable Virtual Face Generator (IVFG) to generate the virtual face
images. The IVFG projects the latent vectors of the original face images into
virtual ones according to a user specific key, based on which the virtual face
images are generated. To make the virtual face images identifiable, we propose
a multi-task learning objective as well as a triplet styled training strategy
to learn the IVFG. Various experiments demonstrate the effectiveness of the
IVFG for generate identifiable virtual face images.
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