  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. We evaluate the performance of our virtual face images using
different face recognizers on diffident face image datasets, all of which
demonstrate the effectiveness of the IVFG for generate identifiable virtual
face images.
