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International Symposium on Neural Networks

ISNN 2009: Advances in Neural Networks – ISNN 2009 pp 356–364Cite as

Feature-Level Fusion of Iris and Face for Personal Identification

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5553))

Abstract

Feature-level fusion remains a challenging problem for multimodal biometrics. However, existing fusion schemes such as sum rule and weighted sum rule are inefficient in complicated condition. In this paper, we propose an efficient feature-level fusion algorithm for iris and face in parallel. The algorithm first normalizes the original features of iris and face using z-score model, and then take complex FDA as the classifier of unitary space. The proposed algorithm is tested using CASIA iris database and two face databases (ORL database and Yale database). Experimental results show the effectiveness of the proposed algorithm.

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Author information

Authors and Affiliations

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

    Zhifang Wang, Qi Han & Xiamu Niu

  2. Norwegian Information Security laboratory, Gjøvik University College, Gjøvik, Norway

    Christoph Busch

Authors
  1. Zhifang Wang
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  2. Qi Han
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  3. Xiamu Niu
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  4. Christoph Busch
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Editor information

Editors and Affiliations

  1. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering, South Dakota School of Mines & Technology, 501 E. St. Joseph Street, SD 57701, Rapid City, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Wang, Z., Han, Q., Niu, X., Busch, C. (2009). Feature-Level Fusion of Iris and Face for Personal Identification. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_38

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  • DOI: https://doi.org/10.1007/978-3-642-01513-7_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01512-0

  • Online ISBN: 978-3-642-01513-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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