A Novel Multimodal Biometrics Recognition Model Based on Stacked ELM and CCA Methods

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2018
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Abstract
Multimodal biometrics combine a variety of biological features to have a significant impact on identification performance, which is a newly developed trend in biometrics identification technology. This study proposes a novel multimodal biometrics recognition model based on the stacked extreme learning machines (ELMs) and canonical correlation analysis (CCA) methods. The model, which has a symmetric structure, is found to have high potential for multimodal biometrics. The model works as follows. First, it learns the hidden-layer representation of biological images using extreme learning machines layer by layer. Second, the canonical correlation analysis method is applied to map the representation to a feature space, which is used to reconstruct the multimodal image feature representation. Third, the reconstructed features are used as the input of a classifier for supervised training and output. To verify the validity and efficiency of the method, we adopt it for new hybrid datasets obtained from typical face image datasets and finger-vein image datasets. Our experimental results demonstrate that our model performs better than traditional methods.
Reference Key
han2018symmetrya Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jucheng Yang,Wenhui Sun,Na Liu,Yarui Chen,Yuan Wang,Shujie Han;Jucheng Yang;Wenhui Sun;Na Liu;Yarui Chen;Yuan Wang;Shujie Han;
Journal Symmetry
Year 2018
DOI
10.3390/sym10040096
URL
https://sciprofiles.com/publication/view/a95156eba6b20378060e66819e9e95d2
Keywords
canonical correlation analysis extreme learning machine multimodal biometrics deep network canonical correlation analysis extreme learning machine multimodal biometrics deep network

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