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An accurate multi-modal biometric identification system for person identification via fusion of face and finger print

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Abstract

Internet of things (IoT) have entirely revolutionized the industry. However, the cyber-security of IoT enabled cyber-physical systems is still one of the main challenges. The success of cyber-physical system is highly reliant on its capability to withstand cyberattacks. Biometric identification is the key factor responsible for the provision of secure cyber-physical system. The conventional unimodal biometric systems do not have the potential to provide the required level of security for cyber-physical system. The unimodal biometric systems are affected by a variety of issues like noisy sensor data, non-universality, susceptibility to forgery and lack of invariant representation. To overcome these issues and to provide higher-security enabled cyber-physical systems, the combination of different biometric modalities is required. To ensure a secure cyber-physical system, a novel multi-modal biometric system based on face and finger print is proposed in this work. Finger print matching is performed using alignment-based elastic algorithm. For the improved facial feature extraction, extended local binary patterns (ELBP) are used. For the effective dimensionality reduction of extracted ELBP feature space, local non-negative matrix factorization is used. Score level fusion is performed for the fusion. Experimental evaluation is done on FVC 2000 DB1, FVC 2000 DB2, ORL (AT&T) and YALE databases. The proposed method achieved a high recognition accuracy of 99.59%.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61872241 and Grant 61572316, in part by the National Key Research and Development Program of China under Grant 2017YFE0104000 and Grant 2016YFC1300302, in part by the Macau Science and Technology Development Fund under Grant 0027/2018/A1, and in part by the Science and Technology Commission of Shanghai Municipality under Grant 18410750700, Grant 17411952600, and Grant 16DZ0501100.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Sidra Aleem, Saleha Masood & Bin Sheng

  2. Department of Computer Science, Liverpool John Moores University, Liverpool, England

    Po Yang

  3. Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Ping Li

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  1. Sidra Aleem
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  2. Po Yang
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Correspondence to Bin Sheng.

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This article belongs to the Topical Collection: Special Issue on Smart Computing and Cyber Technology for Cyberization

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Aleem, S., Yang, P., Masood, S. et al. An accurate multi-modal biometric identification system for person identification via fusion of face and finger print. World Wide Web 23, 1299–1317 (2020). https://doi.org/10.1007/s11280-019-00698-6

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