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

• Published in: Symmetry (Basel) 2018-04, Vol.10 (4), p.96
• Main Authors: Yang, Jucheng, Sun, Wenhui, Liu, Na, Chen, Yarui, Wang, Yuan, Han, Shujie
• Format: Article
• Language: English
• Subjects: Biometric recognition systems; Biometrics; canonical correlation analysis; Correlation analysis; Datasets; deep network; extreme learning machine; Image reconstruction; multimodal biometrics; Neural networks; Representations
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym10040096

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.