Name
Title | ||
|---|---|---|
FD-GAN: Face-demorphing generative adversarial network for restoring accomplice's facial image. |
Abstract | ||
|---|---|---|
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 accompliceu0027s facial restoration remains a challenging problem. In this paper, a face de-morphing generative adversarial network (FD-GAN) is proposed to restore the accompliceu0027s 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 criminalu0027s identity) from the face recognition system and the morphed image stored in the e-passport system (containing both criminal and accompliceu0027s identities), the FD-GAN can effectively restore the accompliceu0027s 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. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Journal |
Volume | Citations | PageRank |
abs/1811.07665 | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Peng | 1 | 360 | 38.79 |
| Le-Bing Zhang | 2 | 1 | 0.35 |
| Min Long | 3 | 179 | 23.63 |