Score-level multibiometric fusion based on Dempster-Shafer theory incorporating uncertainty factors

Nguyen Thanh, Kien, Denman, Simon, Sridharan, Sridha, & Fookes, Clinton (2014) Score-level multibiometric fusion based on Dempster-Shafer theory incorporating uncertainty factors. IEEE Transactions on Human-Machine Systems.

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Abstract

While existing multi-biometic Dempster-Shafer the- ory fusion approaches have demonstrated promising perfor- mance, they do not model the uncertainty appropriately, sug- gesting that further improvement can be achieved. This research seeks to develop a unified framework for multimodal biometric fusion to take advantage of the uncertainty concept of Dempster- Shafer theory, improving the performance of multi-biometric authentication systems. Modeling uncertainty as a function of uncertainty factors affecting the recognition performance of the biometric systems helps to address the uncertainty of the data and the confidence of the fusion outcome. A weighted combination of quality measures and classifiers performance (Equal Error Rate) are proposed to encode the uncertainty concept to improve the fusion. We also found that quality measures contribute unequally to the recognition performance, thus selecting only significant factors and fusing them with a Dempster-Shafer approach to generate an overall quality score play an important role in the success of uncertainty modeling. The proposed approach achieved a competitive performance (approximate 1% EER) in comparison with other Dempster-Shafer based approaches and other conventional fusion approaches.

Impact and interest:

2 citations in Scopus
5 citations in Web of Science®
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ID Code: 78115
Item Type: Journal Article
Refereed: Yes
Keywords: multi-biometrics, biometric fusion, quality-based fusion, Dempster-Shafer fusion, Biosecure DS2
DOI: 10.1109/THMS.2014.2361437
ISSN: 2168-2291
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 IEEE
Deposited On: 29 Oct 2014 23:45
Last Modified: 03 Dec 2014 22:53

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