Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18572
Title: Multimodal biometrics using geometry preserving projections
Authors: Zhang, T
Li, X
Tao, D
Yang, J
Keywords: Geometry preserving projections
Multimodal biometrics
Subspace selection
Issue Date: 2008
Publisher: Elsevier
Source: Pattern recognition, 2008, v. 41, no. 3, p. 805-813 How to cite?
Journal: Pattern recognition 
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.
URI: http://hdl.handle.net/10397/18572
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2007.06.035
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