  Massive captured face images are stored in the database for the
identification of individuals. However, these images can be observed
intentionally or unintentionally by data managers, which is not at the will of
individuals and may cause privacy violations. Existing protection schemes can
maintain identifiability but slightly change the facial appearance, rendering
it still susceptible to the visual perception of the original identity by data
managers. In this paper, we propose an effective identity hider for human
vision protection, which can significantly change appearance to visually hide
identity while allowing identification for face recognizers. Concretely, the
identity hider benefits from two specially designed modules: 1) The virtual
face generation module generates a virtual face with a new appearance by
manipulating the latent space of StyleGAN2. In particular, the virtual face has
a similar parsing map to the original face, supporting other vision tasks such
as head pose detection. 2) The appearance transfer module transfers the
appearance of the virtual face into the original face via attribute
replacement. Meanwhile, identity information can be preserved well with the
help of the disentanglement networks. In addition, diversity and background
preservation are supported to meet the various requirements. Extensive
experiments demonstrate that the proposed identity hider achieves excellent
performance on privacy protection and identifiability preservation.
