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Occlusion-Robust Face Detection Using Shallow and Deep Proposal Based Faster R-CNN Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Cross-Database Face Antispoofing with Robust Feature Representation

Authors : Keyurkumar Patel, Hu Han, Anil K. Jain

Published in: Biometric Recognition

Publisher: Springer International Publishing

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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.

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previous chapter Local Dual-Cross Ternary Pattern for Feature Representation
next chapter Deep Representations Based on Sparse Auto-Encoder Networks for Face Spoofing Detection
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Metadata
Title
Cross-Database Face Antispoofing with Robust Feature Representation
Authors
Keyurkumar Patel
Hu Han
Anil K. Jain
Copyright Year
2016
Book
Biometric Recognition

Print ISBN: 978-3-319-46653-8

Electronic ISBN: 978-3-319-46654-5

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46654-5_67

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