Cross-Database Face Antispoofing with Robust Feature Representation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016-09-21

AUTHORS

Keyurkumar Patel , Hu Han , Anil K. Jain

ABSTRACT

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. More... »

PAGES

611-619

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-46654-5_67

DOI

http://dx.doi.org/10.1007/978-3-319-46654-5_67

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084848142


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