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Quality based adaptive score fusion approach for multimodal biometric system

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Abstract

Multimodal Biometric Systems are extensively employed over unimodal counterparts for user authentication in the digital world. However, the application of multimodal systems to security-critical applications is limited mainly due to non-adaptiveness of these systems to the dynamic environment and inability to distinguish between spoofing attack and the noisy input image. In order to address these issues, a multimodal biometric system, which adaptively combines the scores from individual classifiers is proposed. For this, three modalities viz. face, finger, and iris are used to extract individual classifier scores. These classifier scores are adaptively fused considering that concurrent modalities are boosted and discordant modalities are suppressed. The conflicting belief among classifiers is resolved not only to achieve optimum fusion of classifier scores but also to cater dynamic environment. The proposed quality based score fusion also distinguish between spoofing attacks and noisy inputs as well. The performance of the proposed multimodal biometric system is experimentally validated using three chimeric multimodal databases. On an average, the proposed system achieves an accuracy of 99.5%, an EER of 0.5% and also outperforms state-of-the-art methods.

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Authors and Affiliations

  1. Department of Computer Science, Delhi Technological University, New Delhi, 110042, India

    Keshav Gupta

  2. Defence Research and Development Organization, Ministry of Defence, New Delhi, 110054, India

    Gurjit Singh Walia

  3. Department of Information Technology, Delhi Technological University, New Delhi, 110042, India

    Kapil Sharma

Authors
  1. Keshav Gupta
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  2. Gurjit Singh Walia
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  3. Kapil Sharma
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Correspondence to Kapil Sharma.

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Gupta, K., Walia, G.S. & Sharma, K. Quality based adaptive score fusion approach for multimodal biometric system. Appl Intell 50, 1086–1099 (2020). https://doi.org/10.1007/s10489-019-01579-1

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