Cross-Database Face Antispoofing with Robust Feature Representation

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• 关键词：Face liveness detection ; Cross ; database generalizability ; Deep texture feature ; Eye ; blinking detection
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9967
• 期：1
• 页码：611-619
• 全文大小：1,359 KB
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• 作者单位：Keyurkumar Patel (21)
  Hu Han (22)
  Anil K. Jain (21)
  
  21. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
  22. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China
  
• 丛书名：Biometric Recognition
• ISBN：978-3-319-46654-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9967

文摘

With the wide applications of user authentication based on face recognition, face spoof attacks against face recognition systems are drawing increasing attentions. While emerging approaches of face antispoofing have been reported in recent years, most of them limit to the non-realistic intra-database testing scenarios instead of the cross-database testing scenarios. We propose a robust representation integrating deep texture features and face movement cue like eye-blink as countermeasures for presentation attacks like photos and replays. We learn deep texture features from both aligned facial images and whole frames, and use a frame difference based approach for eye-blink detection. A face video clip is classified as live if it is categorized as live using both cues. Cross-database testing on public-domain face databases shows that the proposed approach significantly outperforms the state-of-the-art.