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A hybrid biometric identification framework for high security applications

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Abstract

Research on biometrics for high security applications has not attracted as much attention as civilian or forensic applications. Limited research and deficient analysis so far has led to a lack of general solutions and leaves this as a challenging issue. This work provides a systematic analysis and identification of the problems to be solved in order to meet the performance requirements for high security applications, a double low problem. A hybrid ensemble framework is proposed to solve this problem. Setting an adequately high threshold for each matcher can guarantee a zero false acceptance rate (FAR) and then use the hybrid ensemble framework makes the false reject rate (FRR) as low as possible. Three experiments are performed to verify the effectiveness and generalization of the framework. First, two fingerprint verification algorithms are fused. In this test only 10.55% of fingerprints are falsely rejected with zero false acceptance rate, this is significantly lower than other state of the art methods. Second, in face verification, the framework also results in a large reduction in incorrect classification. Finally, assessing the performance of the framework on a combination of face and gait verification using a heterogeneous database show this framework can achieve both 0% false rejection and 0% false acceptance simultaneously.

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, 250101, China

    Xuzhou Li, Yilong Yin, Yanbin Ning, Gongping Yang & Lei Pan

  2. Key Laboratory of Information Security and Intelligent Control of Shandong Province, Shandong Youth University of Political Science, Jinan, 250103, China

    Xuzhou Li

Authors
  1. Xuzhou Li
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  2. Yilong Yin
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  3. Yanbin Ning
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  4. Gongping Yang
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  5. Lei Pan
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Corresponding author

Correspondence to Yilong Yin.

Additional information

Xuzhou Li received his BS of computer science and technology from Shandong Institute of Light Industry, China in 2002, and MS of software engineering from Qilu Software College, Shandong University, China in 2006. Li has been working at Shandong Youth College, China since 2002. Now he is also a candidate for PhD of computer science and technology in Shandong University, China now. His research interest is biometrics.

Yilong Yin is now the director of MLA Group and a professor of Shandong University, China. He received his PhD of mechanics in 2000 from Jilin University, China. From 2000 to 2002, he worked as a post-doctoral fellow in the Department of Electronic Science and Engineering, Nanjing University, China. His research interests include machine learning, data mining, computational medicine and biometrics.

Yanbin Ning received his BS of computer science and technology from Software College, Shandong University, China in 2009, from where he also received his MS in 2012. Now he works in China Citic Bank Corporation Limited. His research interest is in biometrics.

Gongping Yang received his PhD in computer science and technology from Shandong University, China in 2007. From 2003 to 2007, he was an professor in the School of Computer Science and Technology, Shandong University, China. His research interests are machine learning and applications, medical image process and analysis, and pattern recognition.

Lei Pan received his BS in computer science and technology from the School of Computer Science and Technology, Shandong University, China in 2009, where he also received his MS in 2012. Now he works in China Citic Bank Corporation Limited. His research interests include face recognition and machine learning.

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Li, X., Yin, Y., Ning, Y. et al. A hybrid biometric identification framework for high security applications. Front. Comput. Sci. 9, 392–401 (2015). https://doi.org/10.1007/s11704-014-4070-1

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  • DOI: https://doi.org/10.1007/s11704-014-4070-1

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