Increase the Security of Multibiometric Systems by Incorporating a Spoofing Detection Algorithm in the Fusion Mechanism

By Emanuela Marasco, Peter A. Johnson, Carlo Sansone, Stephanie A. C. Schuckers. 2011

35 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

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Citations in the corpus

Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems (2016, background): “Accordingly, another interesting future extension of this work may be to exploit our meta-model in the context of recent approaches that combine liveness detection and matching algorithms, instead of using them as independent modules [49]–[52].”
Robust multimodal face and fingerprint fusion in the presence of spoofing attacks (2016, methodology): “The standard approach, as outlined in [5], has been to reject spoofed samples before comparing them against the gallery template.”
A Survey on Anti-Spooﬁng Schemes for Fingerprint Recognition Systems (2014, methodology): “The approach proposed in [Marasco et al. 2011] demonstrated that a more robust fusion can be realized by incorporating a ﬁngerprint liveness detection algorithm in the combination scheme.”
Anti-spoofing in Action: Joint Operation with a Verification System (2013, methodology): “In [17], an anti-spoofing mechanism is employed before the verification stage on a multibiometric system composed of three fingerprint and one face modality.”
Combining match scores with liveness values in a fingerprint verification system (2012, background): “While previous research has considered the problem of designing spoof-resilient fusion schemes for multibiometric systems [20], [19], [7], [10], the specific problem of combining liveness values with match scores has not been investigated.”
Biometrics Evaluation Under Spoofing Attacks (2014, background): “Taking advantage of the fact that the licit and spoof scenario share the same positive class, many publications choose a threshold to achieve a particular desired value of FRR [12], [28]–[32].”
Towards anomaly detection for increased security in multibiometric systems: Spoofing-resistant 1-median fusion eliminating outliers (2014, background): “In the first case, spoofdetected evidence is typically excluded from fusion [10].”
Biometric Antispoofing Methods: A Survey in Face Recognition (2014, background): “Although it is not yet generally deployed, an evaluation protocol which is gaining popularity for the assessment of biometric spoofing, defines two possible working scenarios [80], [107], [164], [182], [183]: • Licit Scenario, considered in classic recognition evaluations, it only takes into account”