FD-GAN: Face De-Morphing Generative Adversarial Network for Restoring Accomplice's Facial Image
FD-GAN: Face De-Morphing Generative Adversarial Network for Restoring Accomplice's Facial Image
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DOI：
10.1109/access.2019.2920713
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Long, Min
中科院分区：
文献类型：
--
作者：
Peng, Fei;Zhang, Le-Bing;Long, Min
人脸变形攻击已被证明是对现有人脸识别系统的严重威胁。虽然已经提出了几种人脸变形检测方法，但人脸变形共犯的人脸恢复仍然是一个具有挑战性的问题。本文提出了一种人脸去变形生成性对抗网络(FD-GAN)来恢复同伙的面部图像。它利用对称的双网络结构和两级恢复损失来分离变形共犯的身份特征。通过利用从人脸识别系统捕获的面部图像(包含罪犯的身份)和存储在电子护照系统中的变形图像(包含罪犯和同伙的身份)，l-D-GAN可以有效地恢复同伙的面部图像。实验结果和分析验证了该方案的有效性。在刑事侦查和司法取证中，它在人脸变形攻击同伙的身份追踪方面具有很大的应用潜力。
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 the 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), thel-D-GAN can effectively restore the accomplice's facial image. The experimental results and analysis demonstrate the effectiveness of the proposed scheme. It has great potential to be applied for tracing the identity of face morphing attack's accomplice in criminal investigation and judicial forensics.