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Towards anomaly detection for increased security in multibiometric systems: spoofing-resistant 1-median fusion eliminating outliers

Wild, P., Radu, P., Chen, L. and Ferryman, J. (2014) Towards anomaly detection for increased security in multibiometric systems: spoofing-resistant 1-median fusion eliminating outliers. In: International Joint Conference on Biometrics (IJCB2014), September 29 - October 2, 2014, Clearwater, Florida, pp. 1-6.

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Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumb...

Abstract/Summary

Multibiometrics aims at improving biometric security in presence of spoofing attempts, but exposes a larger availability of points of attack. Standard fusion rules have been shown to be highly sensitive to spoofing attempts – even in case of a single fake instance only. This paper presents a novel spoofing-resistant fusion scheme proposing the detection and elimination of anomalous fusion input in an ensemble of evidence with liveness information. This approach aims at making multibiometric systems more resistant to presentation attacks by modeling the typical behaviour of human surveillance operators detecting anomalies as employed in many decision support systems. It is shown to improve security, while retaining the high accuracy level of standard fusion approaches on the latest Fingerprint Liveness Detection Competition (LivDet) 2013 dataset.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:48397

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