Name
Abstract | ||
|---|---|---|
Recently, researchers found that the intended generalizability of (deep) face recognition systems increases their vulnerability against attacks. In particular, the attacks based on morphed face images pose a severe security risk to face recognition systems. In the last few years, the topic of (face) image morphing and automated morphing attack detection has sparked the interest of several research laboratories working in the field of biometrics and many different approaches have been published. In this paper, a conceptual categorization and metrics for an evaluation of such methods are presented, followed by a comprehensive survey of relevant publications. In addition, technical considerations and tradeoffs of the surveyed methods are discussed along with open issues and challenges in the field. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2899367 | IEEE ACCESS |
Keywords | Field | DocType |
Biometrics,face morphing attack,face recognition,image morphing,morphing attack detection | Generalizability theory,Data science,Facial recognition system,Categorization,Morphing,Computer science,Biometrics,Distributed computing,Vulnerability | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ulrich Scherhag | 1 | 16 | 3.87 |
| Christian Rathgeb | 2 | 551 | 55.72 |
| Johannes Merkle | 3 | 75 | 12.14 |
| Ralph Breithaupt | 4 | 0 | 0.34 |
| Christoph Busch | 5 | 9 | 3.86 |