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2017 | OriginalPaper | Chapter

POSTER: Non-intrusive Face Spoofing Detection Based on Guided Filtering and Image Quality Analysis

Authors : Fei Peng, Le Qin, Min Long

Published in: Security and Privacy in Communication Networks

Publisher: Springer International Publishing

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Abstract

Aiming to counterstrike the spoofing attacks in face recognition system, a non-intrusive face spoofing detection method based on guided filtering and image quality analysis is proposed. Guided image filtering (GIF) is first implemented for the enhancement of texture component of facial image, and then the local texture features are extracted by calculating local binary patterns (LBP). Meanwhile, the global facial image quality features are obtained from image quality measures. With these features, the spoofing detection is accomplished by using support vector machine (SVM) classifier. Experiments results indicate its effectiveness and it has great potential to be applied for the authenticity verification in face recognition system.

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previous chapter Exploitation of NetEm Utility for Non-payload-based Obfuscation Techniques Improving Network Anomaly Detection
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Metadata
Title
POSTER: Non-intrusive Face Spoofing Detection Based on Guided Filtering and Image Quality Analysis
Authors
Fei Peng
Le Qin
Min Long
Copyright Year
2017
Book
Security and Privacy in Communication Networks

Print ISBN: 978-3-319-59607-5

Electronic ISBN: 978-3-319-59608-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59608-2_49

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