In the present chapter, after a thorough review of state-of-the-art in biometric antispoofing, we present a software-based spoof detection prototype for mobile devices, named MoBio_LivDet (Mobile Biometric Liveness Detection) that can be used in multiple biometric systems. MoBio_LivDet analyzes local features and global structures of face, iris and fingerprint biometric images using a set of low-level feature descriptors and decision-level fusion. In particular, we propose to use image descriptor classification algorithms Locally Uniform Comparison Image Descriptor (LUCID) [15], CENsus TRansform hISTogram (CENTRIST) [16] and Patterns of Oriented Edge Magnitudes (POEM) [17] for face, iris and fingerprint spoof detection. The proposed system allows user to choose “Security Level” (SL) against spoofing, between “low, " “medium” and “high.” Depending on SL, the system selects unitdescriptor or multidescriptors-fusion-based liveness detection. These descriptors are computationally inexpensive, fast and novel approach to real-time image description, which are desirable requisites for mobile processors. Experiments on publicly available data sets containing several real and spoofed faces, irises and fingerprints show promising results. Chapter Contents: • 15.1 Introduction • 15.2 Biometric antispoofing • 15.2.1 State-of-the-art in face antispoofing • 15.2.2 State-of-the-art in fingerprint antispoofing • 15.2.3 State-of-the-art in iris antispoofing • 15.3 Case study: MoBio_LivDet system • 15.3.1 Experiments • 15.4 Research opportunities • 15.4.1 Mobile liveness detection • 15.4.2 Mobile biometric spoofing databases • 15.4.3 Generalization to unknown attacks • 15.4.4 Randomizing input biometric data • 15.4.5 Fusion of biometric system and countermeasures • 15.5 Conclusion • References.

Biometric antispoofing on mobile devices

Micheloni C.;Foresti G. L.
2017

Abstract

In the present chapter, after a thorough review of state-of-the-art in biometric antispoofing, we present a software-based spoof detection prototype for mobile devices, named MoBio_LivDet (Mobile Biometric Liveness Detection) that can be used in multiple biometric systems. MoBio_LivDet analyzes local features and global structures of face, iris and fingerprint biometric images using a set of low-level feature descriptors and decision-level fusion. In particular, we propose to use image descriptor classification algorithms Locally Uniform Comparison Image Descriptor (LUCID) [15], CENsus TRansform hISTogram (CENTRIST) [16] and Patterns of Oriented Edge Magnitudes (POEM) [17] for face, iris and fingerprint spoof detection. The proposed system allows user to choose “Security Level” (SL) against spoofing, between “low, " “medium” and “high.” Depending on SL, the system selects unitdescriptor or multidescriptors-fusion-based liveness detection. These descriptors are computationally inexpensive, fast and novel approach to real-time image description, which are desirable requisites for mobile processors. Experiments on publicly available data sets containing several real and spoofed faces, irises and fingerprints show promising results. Chapter Contents: • 15.1 Introduction • 15.2 Biometric antispoofing • 15.2.1 State-of-the-art in face antispoofing • 15.2.2 State-of-the-art in fingerprint antispoofing • 15.2.3 State-of-the-art in iris antispoofing • 15.3 Case study: MoBio_LivDet system • 15.3.1 Experiments • 15.4 Research opportunities • 15.4.1 Mobile liveness detection • 15.4.2 Mobile biometric spoofing databases • 15.4.3 Generalization to unknown attacks • 15.4.4 Randomizing input biometric data • 15.4.5 Fusion of biometric system and countermeasures • 15.5 Conclusion • References.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11390/1213116
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