VAE/WGAN-Based Image Representation Learning For Pose-Preserving Seamless Identity Replacement In Facial Images

• URL: http://arxiv.org/abs/2003.00641v1
• Date: Mon, 2 Mar 2020 03:35:59 GMT
• Title: VAE/WGAN-Based Image Representation Learning For Pose-Preserving Seamless Identity Replacement In Facial Images
• Authors: Hiroki Kawai, Jiawei Chen, Prakash Ishwar, Janusz Konrad
• Abstract summary: We present a novel variational generative adversarial network (VGAN) based on Wasserstein loss. We show that our network can be used to perform pose-preserving identity morphing and identity-preserving pose morphing.
• Score: 15.855376604558977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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