MixNet for Generalized Face Presentation Attack Detection
| Autor: | Richa Singh, Sushant Kumar Singh, Akshay Agarwal, Nilay Sanghvi, Mayank Vatsa |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Cryptography and Security Computer science Computer Vision and Pattern Recognition (cs.CV) media_common.quotation_subject Face Presentation Reliability (computer networking) Computer Science - Computer Vision and Pattern Recognition 0211 other engineering and technologies 02 engineering and technology Machine learning computer.software_genre Convolutional neural network Facial recognition system Presentation 0202 electrical engineering electronic engineering information engineering media_common Vulnerability (computing) 021110 strategic defence & security studies business.industry Deep learning 020201 artificial intelligence & image processing Artificial intelligence State (computer science) business Cryptography and Security (cs.CR) computer |
| Zdroj: | ICPR |
| Popis: | The non-intrusive nature and high accuracy of face recognition algorithms have led to their successful deployment across multiple applications ranging from border access to mobile unlocking and digital payments. However, their vulnerability against sophisticated and cost-effective presentation attack mediums raises essential questions regarding its reliability. In the literature, several presentation attack detection algorithms are presented; however, they are still far behind from reality. The major problem with existing work is the generalizability against multiple attacks both in the seen and unseen setting. The algorithms which are useful for one kind of attack (such as print) perform unsatisfactorily for another type of attack (such as silicone masks). In this research, we have proposed a deep learning-based network termed as \textit{MixNet} to detect presentation attacks in cross-database and unseen attack settings. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category. Experiments are performed using multiple challenging face presentation attack databases such as SMAD and Spoof In the Wild (SiW-M) databases. Extensive experiments and comparison with existing state of the art algorithms show the effectiveness of the proposed algorithm. Comment: ICPR 2020, 8 pages, 6 figures, 7 tables |
| Databáze: | OpenAIRE |
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