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

作者：Yang, Jucheng; Sun, Wenhui; Liu, Na^*; Chen, Yarui; Wang, Yuan; Han, Shujie

来源：Symmetry-Basel, 2018, 10(4): 96.

DOI：10.3390/sym10040096

• multimodal biometrics
• extreme learning machine
• canonical correlation analysis
• deep network

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.

• 出版日期2018-4
• 单位天津科技大学