Multimodal biometrics using geometry preserving projections

Zhang, T. and Li, Xuelong and Tao, D. and Yang, J. (2008) Multimodal biometrics using geometry preserving projections. Pattern Recognition 41 (3), pp. 805-813. ISSN 0031-3203.

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Official URL: http://dx.doi.org/10.1016/j.patcog.2007.06.035

Abstract

Multimodal biometric system utilizes two or more individual modalities, e.g., face, gait, and fingerprint, to improve the recognition accuracy of conventional unimodal methods. However, existing multimodal biometric methods neglect interactions of different modalities during the subspace selection procedure, i.e., the underlying assumption is the independence of different modalities. In this paper, by breaking this assumption, we propose a Geometry Preserving Projections (GPP) approach for subspace selection, which is capable of discriminating different classes and preserving the intra-modal geometry of samples within an identical class. With GPP, we can project all raw biometric data from different identities and modalities onto a unified subspace, on which classification can be performed. Furthermore, the training stage is carried out once and we have a unified transformation matrix to project different modalities. Unlike existing multimodal biometric systems, the new system works well when some modalities are not available. Experimental results demonstrate the effectiveness of the proposed GPP for individual recognition tasks.

Metadata

Item Type:	Article
Keyword(s) / Subject(s):	Multimodal biometrics, geometry preserving projections, subspace selection
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Administrator
Date Deposited:	07 Feb 2011 11:57
Last Modified:	09 Aug 2023 12:30
URI:	https://eprints.bbk.ac.uk/id/eprint/1864

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