A unimodal fusion framework utilizing multiple enrolment images

Baig, Asim, Kurugollu, Fatih and Bouridane, Ahmed (2013) A unimodal fusion framework utilizing multiple enrolment images. In: Proceedings of 2013 10th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2013. IEEE, Piscataway, NJ, pp. 99-102. ISBN 9781467344258

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Official URL: http://dx.doi.org/10.1109/IBCAST.2013.6512138

Abstract

One of the main purposes of using multiple enrolment images in a biometric recognition system is that if the input image is noisy and/or distorted in some way or is deformed during capturing stage, the recognition system may still be able to match with the genuine enrolment image providing more than one option is available. This approach, as used in conventional unimodal recognition systems, attempts to match the input image with multiple enrolment images for each user and select the highest matching score as the correct matching result. However, there exist some inter-dependencies between the data and matching scores which can be exploited not only to enhance the recognition results but also to achieve a better consistency of matching. In this paper we propose an approach to compute and utilize the relationship between the matching scores and hence exploits the computed inter-dependency measures to (i) achieve an improved and stable recognition results and (ii) provide a strong quality measure that can be employed not only to reduce Error Rates but also to act as a parameter that can be used to set weights for multimodal fusion base recognition systems. To demonstrate the effectiveness of the proposed methods, extensive experiments were carried out on fingerprint and iris data using widely used databases.

Item Type: Book Section
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Becky Skoyles
Date Deposited: 20 Jan 2015 14:36
Last Modified: 10 Aug 2015 11:10
URI: http://nrl.northumbria.ac.uk/id/eprint/20780

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