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Computational Intelligence: Theories, Applications and Future Directions - Volume II pp 465–476Cite as

Reasoning for Uncertainty and Rough Set-Based Approach for an Efficient Biometric Identification: An Application Scenario

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 799))

Abstract

In the theory of knowledge discovery, two fundamental concepts are classification and categories. Some of the specific categories may be definable inside one set of knowledge, but they may be undefinable into another knowledge base. This paper contains significant rough membership functional properties, which are utilized in approximation reasoning of an uncertain and vague concept in a knowledge base. In this paper, we have employed the Indicator Function and performed the reasoning for uncertainty, specifically for the rough membership properties of union and intersection. Along with this, we have utilized rough set theory and proposed an approach for an efficient biometric identification. The complexity and efficiency analysis of our proposed approach is also presented in this paper.

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Author information

Authors and Affiliations

  1. C. R. Rao Advanced Institute of Mathematics, Statistics and Computer Science, University of Hyderabad Campus, Hyderabad, India

    Ajeet Singh, Vikas Tiwari & Appala Naidu Tentu

  2. Indian Institute of Technology Bombay, Mumbai, India

    Priyanka Garg

Authors
  1. Ajeet Singh
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  2. Vikas Tiwari
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  3. Priyanka Garg
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  4. Appala Naidu Tentu
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Corresponding author

Correspondence to Ajeet Singh .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Nishchal K. Verma

  2. Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    A. K. Ghosh

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Singh, A., Tiwari, V., Garg, P., Tentu, A.N. (2019). Reasoning for Uncertainty and Rough Set-Based Approach for an Efficient Biometric Identification: An Application Scenario. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume II. Advances in Intelligent Systems and Computing, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-13-1135-2_35

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