RoPAD: Robust Presentation Attack Detection through Unsupervised Adversarial Invariance
| Autor: | Wael AbdAlmageed, Iacopo Masi, Shuai Xia, Ayush Jaiswal |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
021110 strategic defence & security studies biometrics Computer Science - Machine Learning Computer science business.industry Deep learning Computer Vision and Pattern Recognition (cs.CV) 0211 other engineering and technologies Computer Science - Computer Vision and Pattern Recognition deep learning 02 engineering and technology Overfitting Machine learning computer.software_genre Machine Learning (cs.LG) Adversarial system Robustness (computer science) 0202 electrical engineering electronic engineering information engineering face anti spoofing face anti spoofing biometrics deep learning 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | ICB |
| Popis: | For enterprise, personal and societal applications, there is now an increasing demand for automated authentication of identity from images using computer vision. However, current authentication technologies are still vulnerable to presentation attacks. We present RoPAD, an end-to-end deep learning model for presentation attack detection that employs unsupervised adversarial invariance to ignore visual distractors in images for increased robustness and reduced overfitting. Experiments show that the proposed framework exhibits state-of-the-art performance on presentation attack detection on several benchmark datasets. To appear in Proceedings of International Conference on Biometrics (ICB), 2019 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|