Earticle

초록

영어

This paper uses Support Vector Machines (SVM) to fuse multiple classifiers for an offline signature system. From the signature images, global and local features are extracted and the signatures are verified with the help of Gaussian empirical rule, Euclidean and Mahalanobis distance based classifiers. SVM is used to fuse matching scores of these matchers. Finally, recognition of query signatures is done by comparing it with all signatures of the database. The proposed system is tested on a signature database contains 5400 offline signatures of 600 individuals and the results are found to be promising.

목차

Abstract
 1. Introduction
 2. Preprocessing and feature extraction
  2.1 Preprocessing operations
  2.2 Global and local feature extraction
 3. Matching score generation
  3.1 Matching score generation using Euclidean distance
  3.2 Matching score generation using Mahalanobis distance
  3.3 Matching score generation using Gaussian Empirical Rule
 4. Fusion of multiple matchers using Support Vector Machines
 5. Experimental results
 6. Conclusion
 References

키워드

저자

• Dakshina Ranjan Kisku [ Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College ]
• Phalguni Gupta [ Department of Computer Science and Engineering, Indian Institute of Technology Kanpur ]
• Jamuna Kanta Sing [ Department of Computer Science and Engineering, Jadavpur University ]

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