Earticle

초록

영어

Mining of data is the process of discovering patterns from a large data set and uses this knowledge for matching purpose. In this paper a neural network based approach of data mining is used to verify dynamic signature patterns. In an authentication process, everyone may have a signature that is used to legally prove the document and to bind the individual with the inclination contained in the document. Signature verification is the verification process in which a given input is examined and is either rejected as forgery or accepted as genuine. The proposed algorithm is applied to a set of 500 signature samples collected from 20 individuals. Performance of the system is depicted by using three parameters that are accuracy, false acceptance rate (FAR) and false rejection rate (FRR). Experiments are performed by training the system with more and more number of samples. The results show that the system with neural network has better performance as compared to support vector machines.

목차

Abstract
 1. Introduction
 2. Related Work
 3. System Methodology
  3.1 Acquiring Data
  3.2 Enrolment
  3.3 Feature Extraction
  3.4 Handwritten Signature Database
  3.5 Verification
 4. Experimental Results
 5. Conclusion and Future Scope
 References

키워드

저자

• Ankita Wadhawan [ Assistant Professor, Department of Information Technology DAV Institute of Engineering & Technology, Jalandhar, Punjab, India ]
• Avani Bhatia [ Assistant Professor, Department of Information Technology DAV Institute of Engineering & Technology, Jalandhar, Punjab, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605