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Automated offline Arabic signature verification system using multiple features fusion for forensic applications
Other Title(s)
نظام تحقق آيل من التواقيع العربية باستخدام مجموعة من المميزات و الخصائص الخطية المدمجة للتطبيقات الجنائية
Joint Authors
Darwish, Sad M.
al-Nur, Ashraf M.
Source
Arab Journal of Forensic Sciences and Forensic Medicine
Issue
Vol. 1, Issue 4 (31 Dec. 2016), pp.424-437, 14 p.
Publisher
Naif Arab University for Security Sciences Arab Society for Forensic Sciences and Forensic Medicine
Publication Date
2016-12-31
Country of Publication
Saudi Arabia
No. of Pages
14
Main Subjects
Abstract EN
The signature of a person is one of the most popular and legally accepted behavioral biometrics that provides a secure means for verification and personal identification in many applications such as financial, commercial and legal transactions.
The objective of the signature verification system is to classify between genuine and forged signatures that are often associated with intrapersonal and interpersonal variability.
Unlike other languages, Arabic has unique features; it contains diacritics, ligatures, and overlapping.
Because of lacking any form of dynamic information during the Arabic signature’s writing process, it will be more difficult to obtain higher verification accuracy.
This paper addresses the above difficulty by introducing a novel offline Arabic signature verification algorithm.
The key point is using multiple feature fusion with fuzzy modeling to capture different aspects of a signature individually in order to improve the verification accuracy.
State-of-the-art techniques adopt the fuzzy set to describe the properties of the extracted features to handle a signature’s uncertainty; this work also employs the fuzzy variables to describe the degree of similarity of the signature’s features to deal with the ambiguity of questioned document examiner judgment of signature similarity.
It is concluded from the experimental results that the verification system performs well and has the ability to reduce both False Acceptance Rate (FAR) and False Rejection Rate (FRR).
American Psychological Association (APA)
Darwish, Sad M.& al-Nur, Ashraf M.. 2016. Automated offline Arabic signature verification system using multiple features fusion for forensic applications. Arab Journal of Forensic Sciences and Forensic Medicine،Vol. 1, no. 4, pp.424-437.
https://search.emarefa.net/detail/BIM-818876
Modern Language Association (MLA)
Darwish, Sad M.& al-Nur, Ashraf M.. Automated offline Arabic signature verification system using multiple features fusion for forensic applications. Arab Journal of Forensic Sciences and Forensic Medicine Vol. 1, no. 4 (Dec. 2016), pp.424-437.
https://search.emarefa.net/detail/BIM-818876
American Medical Association (AMA)
Darwish, Sad M.& al-Nur, Ashraf M.. Automated offline Arabic signature verification system using multiple features fusion for forensic applications. Arab Journal of Forensic Sciences and Forensic Medicine. 2016. Vol. 1, no. 4, pp.424-437.
https://search.emarefa.net/detail/BIM-818876
Data Type
Journal Articles
Language
English
Record ID
BIM-818876