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Face–iris multimodal biometric scheme based on feature level fusion

[+] Author Affiliations
Guang Huo

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

Northeast Dianli University, Information Office, ChangChun Road, Jilin 132012, China

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

Yuanning Liu, Xiaodong Zhu, Hongxing Dong

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

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

Fei He

Northeast Normal University, School of Computer Science and Information Technology, Renmin Street, Changchun 130117, China

J. Electron. Imaging. 24(6), 063020 (Dec 24, 2015). doi:10.1117/1.JEI.24.6.063020
History: Received August 2, 2015; Accepted November 20, 2015
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Abstract.  Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as well as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion. We propose a face–iris recognition method based on feature level fusion. We build a special two-dimensional-Gabor filter bank to extract local texture features from face and iris images, and then transform them by histogram statistics into an energy-orientation variance histogram feature with lower dimensions and higher distinguishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

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Citation

Guang Huo ; Yuanning Liu ; Xiaodong Zhu ; Hongxing Dong and Fei He
"Face–iris multimodal biometric scheme based on feature level fusion", J. Electron. Imaging. 24(6), 063020 (Dec 24, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.6.063020


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Toward accurate and fast iris segmentation for iris biometrics. IEEE Trans Pattern Anal Mach Intell 2009;31(9):1670-84.
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