1 research outputs found
VAE/WGAN-Based Image Representation Learning For Pose-Preserving Seamless Identity Replacement In Facial Images
We present a novel variational generative adversarial network (VGAN) based on
Wasserstein loss to learn a latent representation from a face image that is
invariant to identity but preserves head-pose information. This facilitates
synthesis of a realistic face image with the same head pose as a given input
image, but with a different identity. One application of this network is in
privacy-sensitive scenarios; after identity replacement in an image, utility,
such as head pose, can still be recovered. Extensive experimental validation on
synthetic and real human-face image datasets performed under 3 threat scenarios
confirms the ability of the proposed network to preserve head pose of the input
image, mask the input identity, and synthesize a good-quality realistic face
image of a desired identity. We also show that our network can be used to
perform pose-preserving identity morphing and identity-preserving pose
morphing. The proposed method improves over a recent state-of-the-art method in
terms of quantitative metrics as well as synthesized image quality.Comment: 6 pages, 5 figures, 2019 IEEE 29th International Workshop on Machine
Learning for Signal Processing (MLSP