Linear discriminant multi-set canonical correlations analysis (LDMCCA): an efficient approach for feature fusion of finger biometrics

Feature fusion-based multimodal biometrics has become an increasing interest to many researchers in recent years, particularly for finger biometrics. There are, however, many challenges in fusing multiple feature sets, as the case with Canonical Correlation Analysis (CCA) and Multi-set Canonical Cor...

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Bibliographic Details
Published in:Multimedia tools and applications 2015-06, Vol.74 (13), p.4469-4486
Main Authors: Peng, Jialiang, Li, Qiong, Abd El-Latif, Ahmed A., Niu, Xiamu
Format: Article
Language:English
Subjects:
Analysis
Biometrics
Computer Communication Networks
Computer Science
Correlation analysis
Data Structures and Information Theory
Fingerprints
Fingers
Fuses
Human
Methods
Multimedia
Multimedia computer applications
Multimedia Information Systems
Principal components analysis
Special Purpose and Application-Based Systems
Statistical analysis
Studies
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Summary:Feature fusion-based multimodal biometrics has become an increasing interest to many researchers in recent years, particularly for finger biometrics. There are, however, many challenges in fusing multiple feature sets, as the case with Canonical Correlation Analysis (CCA) and Multi-set Canonical Correlation Analysis (MCCA). How to extend them to fuse multiple feature sets is a significant problem in general. In this paper, we propose a novel multimodal finger biometric method, which provides feature fusion approach called linear discriminant multi-set canonical correlation analysis (LDMCCA). It combines finger vein, fingerprint, finger shape and finger knuckle print features of a single human finger. Compared with CCA and MCCA, LDMCCA contains the class information of the training samples and represents the fused features more efficiently and discriminatively in few dimensions. The experimental results on a merged multimodal finger biometric database show that LDMCCA is beneficial to fuse multiple features as well as achieves lower error rates than the existing approaches.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-013-1817-x