A Novel Multimodal Biometrics Recognition Model Based on Stacked ELM and CCA Methods
AbstractMultimodal 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. View Full-Text
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Yang, J.; Sun, W.; Liu, N.; Chen, Y.; Wang, Y.; Han, S. A Novel Multimodal Biometrics Recognition Model Based on Stacked ELM and CCA Methods. Symmetry 2018, 10, 96.
Yang J, Sun W, Liu N, Chen Y, Wang Y, Han S. A Novel Multimodal Biometrics Recognition Model Based on Stacked ELM and CCA Methods. Symmetry. 2018; 10(4):96.Chicago/Turabian Style
Yang, Jucheng; Sun, Wenhui; Liu, Na; Chen, Yarui; Wang, Yuan; Han, Shujie. 2018. "A Novel Multimodal Biometrics Recognition Model Based on Stacked ELM and CCA Methods." Symmetry 10, no. 4: 96.
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