Cross-Database Face Antispoo ng with Robust …

To appear in CCBR, 2016 Cross-Database Face Antispoofing with RobustFeature RepresentationKeyurkumar Patel1, Hu Han2,?, and Anil K. Jain11 Department of Computer Science and Engineering,Michigan State University, East Lansing, MI 48824, USA2 Key Lab of Intelligent Information Processing, Chinese Academy of Sciences (CAS),Institute of Computing Technology, CAS, Beijing 100190, the wide applications of user authentication based onface recognition, face spoof attacks against face recognition systems aredrawing increasing attentions. While emerging approaches of face an-tispoofing have been reported in recent years, most of them limit tothe non-realistic intra-database testing scenarios instead of the Cross-Database testing scenarios. We propose a Robust representation integrat-ing deep texture features and face movement cue like eye-blink as coun-termeasures for presentation attacks like photos and replays.

Cross-database Face Antispoo ng with Robust Features 5 Fig.3. The general-to-speci c deep transfer learning strategy utilizes large databases of image and face classi cation (e.g., ImageNet and WebFace) and a relatively small

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  Action, Deep, Classi, Classi cation, Imagenet

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