Earticle

초록

영어

Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naïve Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used. For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naïve Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naïve Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naïve Bayes Tree and 88.12% (with forgeries).

목차

Abstract
 1. Introduction
 2. Related Work
 3. Proposed Model
  3.1. Datasets Description
  3.2. Preprocessing
  3.3. Post Processing
  3.4. Classifiers
 4. Experimental Setup
 5. Experimental Results
 6. Results Validation
 7. Discussion
 8. Conclusion and Future Work
 References

키워드

저자

• Abdul Salam Shah [ SZABIST, Islamabad, Pakistan ] Corresponding author
• M.N.A. Khan [ SZABIST, Islamabad, Pakistan ]
• Fazli Subhan [ National University of Modern Languages-NUML, Islamabad, Pakistan ]
• Muhammad Fayaz [ University of Malakand, KPK, Pakistan ]
• Asadullah Shah [ International Islamic University Malaysia (IIUM), Malaysia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605