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Linear discriminant multi-set canonical correlations analysis (LDMCCA): an efficient approach for feature fusion of finger biometrics

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Abstract

Feature fusion-based multimodal biometrics has become an increasing interest to many researchers in recent years, particularly for finger biometrics. There are, however, many challenges in fusing multiple feature sets, as the case with Canonical Correlation Analysis (CCA) and Multi-set Canonical Correlation Analysis (MCCA). How to extend them to fuse multiple feature sets is a significant problem in general. In this paper, we propose a novel multimodal finger biometric method, which provides feature fusion approach called linear discriminant multi-set canonical correlation analysis (LDMCCA). It combines finger vein, fingerprint, finger shape and finger knuckle print features of a single human finger. Compared with CCA and MCCA, LDMCCA contains the class information of the training samples and represents the fused features more efficiently and discriminatively in few dimensions. The experimental results on a merged multimodal finger biometric database show that LDMCCA is beneficial to fuse multiple features as well as achieves lower error rates than the existing approaches.

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Acknowledgment

This work is supported by The Fundamental Research Funds for the Central Universities (Grant Number: HIT. NSRIF. 2013061) and Ministry of Scientific Research (Egypt-Tunisia Cooperation Program).

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Author notes

  1. Jialiang Peng

    Present address: Room 1523, Building 2A, Science zone of Harbin Institute of Technology, No.2 Yikuang Street, Nangang District, Harbin, 150080, China

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Jialiang Peng, Qiong Li & Xiamu Niu

  2. Information and Network Administration Center, Heilongjiang University, Harbin, 150080, China

    Jialiang Peng

  3. Mathematics Department, Faculty of Science, Menoufia University, Shebin El-Koom, 32511, Egypt

    Ahmed A. Abd El-Latif

  4. Room 1523, Building 2A, Science zone of Harbin Institute of Technology, No.2 Yikuang Street, Nangang District, Harbin, 150080, China

    Qiong Li & Xiamu Niu

Authors
  1. Jialiang Peng
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  2. Qiong Li
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  3. Ahmed A. Abd El-Latif
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  4. Xiamu Niu
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Correspondence to Qiong Li.

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Peng, J., Li, Q., Abd El-Latif, A.A. et al. Linear discriminant multi-set canonical correlations analysis (LDMCCA): an efficient approach for feature fusion of finger biometrics. Multimed Tools Appl 74, 4469–4486 (2015). https://doi.org/10.1007/s11042-013-1817-x

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  • DOI: https://doi.org/10.1007/s11042-013-1817-x

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