Face morphing attack is proved to be a serious threat to the existing face
recognition systems. Although a few face morphing detection methods have been
put forward, the face morphing accomplice's facial restoration remains a
challenging problem. In this paper, a face de-morphing generative adversarial
network (FD-GAN) is proposed to restore the accomplice's facial image. It
utilizes a symmetric dual network architecture and two levels of restoration
losses to separate the identity feature of the morphing accomplice. By
exploiting the captured facial image (containing the criminal's identity) from
the face recognition system and the morphed image stored in the e-passport
system (containing both criminal and accomplice's identities), the FD-GAN can
effectively restore the accomplice's facial image. Experimental results and
analysis demonstrate the effectiveness of the proposed scheme. It has great
potential to be implemented for detecting the face morphing accomplice in a
real identity verification scenario.

本文提出了一种面部去变形生成对抗网络 (FD-GAN)，用于恢复面部欺诈同伙的图像，该网络具有双重对称结构和两个级别的恢复损失，通过利用面部识别系统捕获的面部图像，以及存储在电子护照系统中的变形图像，FD-GAN 可以有效地恢复同伴的面部图像，实验结果和分析证明了该方案的有效性和实际应用的巨大潜力。