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Journal of Electronic Imaging

Face–iris multimodal biometric scheme based on feature level fusion
Author(s): Guang Huo; Yuanning Liu; Xiaodong Zhu; Hongxing Dong; Fei He
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Paper 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.

Paper Details

Date Published: 24 December 2015
PDF: 10 pages
J. Electron. Imaging. 24(6) 063020 doi: 10.1117/1.JEI.24.6.063020
Published in: Journal of Electronic Imaging Volume 24, Issue 6
Show Author Affiliations
Guang Huo, Jilin Univ. (China)
Northeast Dianli Univ. (China)
Yuanning Liu, Jilin Univ. (China)
Xiaodong Zhu, Jilin Univ. (China)
Hongxing Dong, Jilin Univ. (China)
Fei He, Northeast Normal Univ. (China)

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