University of Surrey

Test tubes in the lab Research in the ATI Dance Research

A Unified framework for biometric expert fusion incorporating quality measures

Poh, N and Kittler, J (2012) A Unified framework for biometric expert fusion incorporating quality measures IEEE Transactions on Pattern Analysis and Machine Intelligence, 34 (1). pp. 3-18.

Full text not available from this repository.

Abstract

This paper proposes a unified framework for quality-based fusion of multimodal biometrics. Quality-dependent fusion algorithms aim to dynamically combine several classifier (biometric expert) outputs as a function of automatically derived (biometric) sample quality. Quality measures used for this purpose quantify the degree of conformance of biometric samples to some predefined criteria known to influence the system performance. Designing a fusion classifier to take quality into consideration is difficult because quality measures cannot be used to distinguish genuine users from impostors, i.e., they are nondiscriminative yet still useful for classification. We propose a general Bayesian framework that can utilize the quality information effectively. We show that this framework encompasses several recently proposed quality-based fusion algorithms in the literatureNandakumar et al., 2006; Poh et al., 2007; Kryszczuk and Drygajo, 2007; Kittler et al., 2007; Alonso-Fernandez, 2008; Maurer and Baker, 2007; Poh et al., 2010. Furthermore, thanks to the systematic study concluded herein, we also develop two alternative formulations of the problem, leading to more efficient implementation (with fewer parameters) and achieving performance comparable to, or better than, the state of the art. Last but not least, the framework also improves the understanding of the role of quality in multiple classifier combination. © 2012 IEEE.

Item Type: Article
Authors :
NameEmailORCID
Poh, Nn.poh@surrey.ac.ukUNSPECIFIED
Kittler, JUNSPECIFIEDUNSPECIFIED
Date : 1 January 2012
Identification Number : 10.1109/TPAMI.2011.102
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 12:17
Last Modified : 17 May 2017 15:02
URI: http://epubs.surrey.ac.uk/id/eprint/834742

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800