PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Unified Framework for Multimodal Biometric Fusion Incorporating Quality Measures
Norman Poh and Josef Kittler
IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 34, Number 1, pp. 3-18, 2012.

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 literature—Nandakumar 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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Multimodal Integration
ID Code:7148
Deposited By:Norman Poh
Deposited On:21 February 2012