Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31604
Title: Represent and fuse bimodal biometric images at the feature level : complex-matrix-based fusion scheme
Authors: Xu, Y
Zhang, D 
Keywords: Biometric images
Feature extraction
Feature level fusion
Multibiometrics
Issue Date: 2010
Publisher: SPIE-International Society for Optical Engineering
Source: Optical engineering, 2010, v. 49, no. 3, 037002 How to cite?
Journal: Optical engineering 
Abstract: Multibiometrics can obtain a higher accuracy than the single biometrics by simultaneously using multiple biometric traits of the subject. We note that biometric traits are usually in the form of images. Thus, how to properly fuse the information of multiple biometric images of the subject for authentication is crucial for multibiometrics. We propose a novel image-based linear discriminant analysis (IBLDA) approach to fuse two biometric traits (i.e., bimodal biometric images) of the same subject in the form of matrix at the feature level. IBLDA first integrates two biometric traits of one subject into a complex matrix and then directly extracts low-dimensional features for the integrated biometric traits. IBLDA also enables more information to be exploited than the matching score level fusion and the decision level fusion. Compared to linear discriminant analysis (LDA), IBLDA has the following advantages: First, it can overcome the small sample size problem that conventional LDA usually suffers from. Second, IBLDA solves the eigenequation at a low computational cost. Third, when storing the scatter matrices IBLDA will not bring as heavy a memory burden as conventional LDA. We also clearly show the theoretical foundation of the proposed method. The experiment result shows that the proposed method can obtain a high classification accuracy.
URI: http://hdl.handle.net/10397/31604
ISSN: 0091-3286
EISSN: 1560-2303
DOI: 10.1117/1.3359514
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

57
Last Week
1
Last month
0
Citations as of Sep 20, 2017

WEB OF SCIENCETM
Citations

19
Last Week
0
Last month
1
Citations as of Sep 21, 2017

Page view(s)

30
Last Week
0
Last month
Checked on Sep 17, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.