NDLI: Non-reference image quality assessment and natural scene statistics to counter biometric sensor spoofing

Content Provider	IET Digital Library
Author	Söllinger, Dominik Trung, Pauline Uhl, Andreas
Abstract	Non-reference image quality measures (IQM) as well as their associated natural scene statistics (NSS) are used to distinguish real biometric data from fake data as used in presentation/sensor spoofing attacks. An experimental study shows that a support vector machine directly trained on NSS as used in blind/referenceless image spatial quality evaluator provides highly accurate classification of real versus fake iris, fingerprint, face, and fingervein data in generic manner. This contrasts to using the IQM directly, the accuracy of which turns out to be rather data set and parameter choice-dependent. While providing very low average classification error rate values for complete training data, generalisation to unseen attack types is difficult in open-set scenarios and obtained accuracy varies in almost unpredictable manner. This implies that for each given sensor/attack set-up, the ability of the introduced methods to detect unseen attacks needs to be assessed separately.
Starting Page	314
Ending Page	324
Page Count	11
ISSN	20474938
Volume Number	7
e-ISSN	20474946
Issue Number	Issue 4, Jul (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/7/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2017.0146
Journal	IET Biometrics
Publisher	The Institution of Engineering and Technology
Publisher Date	2018-01-22
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Associated Natural Scene Statistics Biometric Data Biometric Sensor Spoofing Biomimetics Blind/referenceless Image Spatial Quality Evaluator Complete Training Data Computer Vision And Image Processing Technique Experimental Study Fake Data Feature Extraction Fingerprint Identification Fingervein Data Generic Manner Given Sensor/attack Highly Accurate Classification Image Processing IQM Iris Recognition Knowledge Engineering Technique Learning in AI Low Average Classification Error Rate Value Natural Scenes Nonreference Image Quality Assessment Nonreference Image Quality Measures NSS Open-set Scenario Optical, Image And Video Signal Processing Pattern Classification Presentation/sensor Spoofing Attack Statistical Analysis Statistics Support Vector Machine Unseen Attack Unseen Attack Types Versus Fake Iris
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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