Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28325
Title: A feature extraction method for use with bimodal biometrics
Authors: Xu, Y
Zhang, D 
Yang, JY
Keywords: Biometrics
Feature extraction
Matrix-based complex PCA (MCPCA)
Palmprint identification
Principal component analysis
Issue Date: 2010
Publisher: Elsevier
Source: Pattern recognition, 2010, v. 43, no. 3, p. 1106-1115 How to cite?
Journal: Pattern recognition 
Abstract: Bimodal biometrics has been found to outperform single biometrics and are usually implemented using the matching score level or decision level fusion, though this fusion will enable less information of bimodal biometric traits to be exploited for personal authentication than fusion at the feature level. This paper proposes matrix-based complex PCA (MCPCA), a feature level fusion method for bimodal biometrics that uses a complex matrix to denote two biometric traits from one subject. The method respectively takes the two images from two biometric traits of a subject as the real part and imaginary part of a complex matrix. MCPCA applies a novel and mathematically tractable algorithm for extracting features directly from complex matrices. We also show that MCPCA has a sound theoretical foundation and the previous matrix-based PCA technique, two-dimensional PCA (2DPCA), is only one special form of the proposed method. On the other hand, the features extracted by the developed method may have a large number of data items (each real number in the obtained features is called one data item). In order to obtain features with a small number of data items, we have devised a two-step feature extraction scheme. Our experiments show that the proposed two-step feature extraction scheme can achieve a higher classification accuracy than the 2DPCA and PCA techniques.
URI: http://hdl.handle.net/10397/28325
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2009.09.013
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