Cascaded multimodal biometric recognition framework

Baig, Asim, Albesher, Badr, Kurugollu, Fatih and Bouridane, Ahmed (2014) Cascaded multimodal biometric recognition framework. IET Biometrics, 3 (1). pp. 16-28. ISSN 2047-4938

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Official URL: http://dx.doi.org/10.1049/iet-bmt.2012.0043

Abstract

A practically viable multi-biometric recognition system should not only be stable, robust and accurate but should also adhere to real-time processing speed and memory constraints. This study proposes a cascaded classifier-based framework for use in biometric recognition systems. The proposed framework utilises a set of weak classifiers to reduce the enrolled users?? dataset to a small list of candidate users. This list is then used by a strong classifier set as the final stage of the cascade to formulate the decision. At each stage, the candidate list is generated by a Mahalanobis distance-based match score quality measure. One of the key features of the authors framework is that each classifier in the ensemble can be designed to use a different modality thus providing the advantages of a truly multimodal biometric recognition system. In addition, it is one of the first truly multimodal cascaded classifier-based approaches for biometric recognition. The performance of the proposed system is evaluated both for single and multimodalities to demonstrate the effectiveness of the approach.

Item Type: Article
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Becky Skoyles
Date Deposited: 10 Jul 2014 10:20
Last Modified: 10 Aug 2015 11:05
URI: http://nrl.northumbria.ac.uk/id/eprint/17060

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