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Score level fusion scheme based on adaptive local Gabor features for face-iris-fingerprint multimodal biometric

[+] Author Affiliations
Fei He

Jinlin University, College of Computer Science and Technology, Changchun 130012, China

Jinlin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun 130012, China

Yuanning Liu

Jinlin University, College of Computer Science and Technology, Changchun 130012, China

Jinlin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun 130012, China

Xiaodong Zhu

Jinlin University, College of Computer Science and Technology, Changchun 130012, China

Jinlin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun 130012, China

Chun Huang

Jinlin University, College of Computer Science and Technology, Changchun 130012, China

Jinlin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun 130012, China

Ye Han

Jinlin University, College of Computer Science and Technology, Changchun 130012, China

Jinlin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun 130012, China

Ying Chen

Jinlin University, College of Computer Science and Technology, Changchun 130012, China

Jinlin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun 130012, China

Nanchang Hangkong University, College of Software, Nanchang 330063, China

J. Electron. Imaging. 23(3), 033019 (Jun 27, 2014). doi:10.1117/1.JEI.23.3.033019
History: Received October 27, 2013; Revised April 8, 2014; Accepted May 23, 2014
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Abstract.  A multimodal biometric system has been considered a promising technique to overcome the defects of unimodal biometric systems. We have introduced a fusion scheme to gain a better understanding and fusion method for a face-iris-fingerprint multimodal biometric system. In our case, we use particle swarm optimization to train a set of adaptive Gabor filters in order to achieve the proper Gabor basic functions for each modality. For a closer analysis of texture information, two different local Gabor features for each modality are produced by the corresponding Gabor coefficients. Next, all matching scores of the two Gabor features for each modality are projected to a single-scalar score via a trained, supported, vector regression model for a final decision. A large-scale dataset is formed to validate the proposed scheme using the Facial Recognition Technology database-fafb and CASIA-V3-Interval together with FVC2004-DB2a datasets. The experimental results demonstrate that as well as achieving further powerful local Gabor features of multimodalities and obtaining better recognition performance by their fusion strategy, our architecture also outperforms some state-of-the-art individual methods and other fusion approaches for face-iris-fingerprint multimodal biometric systems.

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Citation

Fei He ; Yuanning Liu ; Xiaodong Zhu ; Chun Huang ; Ye Han, et al.
"Score level fusion scheme based on adaptive local Gabor features for face-iris-fingerprint multimodal biometric", J. Electron. Imaging. 23(3), 033019 (Jun 27, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.3.033019


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