期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 64, 期 -, 页码 93-103出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2016.07.009
关键词
Multimodal biometrics recognition; Local fusion visual feature; Extreme learning machine; Variational Bayesian
类别
资金
- National Natural Science Foundation of China [61402331, 61402332, 61502338]
- key projects of Tianjin science and technology support program [15ZCZDGX00200]
- Tianjin Research Program of Application Foundation and Advanced Technology [14JCYBJC42500, 15JCQNJC00700]
- Foundation of Educational Commission of Tianjin City, China [20140802]
- Open Fund of Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis [GDUPTKLAB201504]
Multimodal biometrics provides rich information in biometric recognition systems, thus a valid multi modal feature fusion framework and an efficient recognition algorithm are desirable for multimodal biometrics systems. In this paper, we design a multimodal fusion framework for face and fingerprint images using block based feature-image matrix, and extract a type of middle-layer semantic feature from local features-a local fusion visual feature, which has better characterization capabilities with lower dimension for multimodal biometrics. Furthermore, we create recognition utilizing the Variational Bayesian Extreme Learning Machine (VBELM), which has an obvious speed advantage by random input weights, and also has superior stability and generalization by adding a non-informative full Gaussian prior. This research enables multimodal biometrics recognition system to have a concentrated fusion feature description and great recognition performance. Experimental results show that the proposed multimodal biometrics recognition system has a higher testing accuracy in comparison to the traditional methods with higher efficiency and better stability. (C) 2016 Elsevier Ltd. All rights reserved.
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