Earticle

초록

영어

Selection of cohort models plays a vital role to increase the accuracy of a biometric authentication system as well as to reduce the computational cost. This paper proposes a novel approach for cohort selection called Max-Min-Centroid-Cluster (MMCC) method. The clusters of cohorts are generated by K-means clustering technique. The union of the clusters having largest and smallest centroid value is taken as cohort subset. The cohort scores, after normalization using different cohort based score normalization techniques, are used in authentication process of the system. Evaluation has been carried out on FEI face datasets. The performance of this novel methodology is analyzed using T-norm and Aggarwal (max rule) normalization techniques. Experimental results exhibit the efficacy of the proposed method.

목차

Abstract
 1. Introduction
 2. Cohort Selection
 3. Score Normalization using Different Normalization Techniques
  3.1. 1T-Norm Normalization:
  3.2. Aggarwal (max rule) Normalization:
 4. Experimental Evaluation
  4.1. Database and Experimental Setup
  4.2. Experimental Results and Performance Evolution
 5. Conclusion and Future Work
 References

키워드

저자

• Jogendra Garain [ Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur – 713209, West Bengal, India ]
• Ravi Kant Kumar [ Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur – 713209, West Bengal, India ]
• Goutam Sanyal [ Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur – 713209, West Bengal, India ]
• Dakshina Ranjan Kisku [ Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur – 713209, West Bengal, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605