Off-line Signature Verification Using Enhanced Modified Direction Features in Conjunction with Neural Classifiers and Support Vector Machines

Author(s)
Nguyen, Vu
Blumenstein, Michael
Muthukkumarasamy, Vallipuram
Leedham, Graham
Publication Date
2007
Abstract
As a biometric, signatures have been widely used to identify people. In the context of static image processing, the lack of dynamic information such as velocity, pressure and the direction and sequence of strokes has made the realization of accurate off-line signature verification systems more challenging as compared to their on-line counterparts. In this paper, we propose an effective method to perform off-line signature verification based on intelligent techniques. Structural features are extracted from the signature's contour using the Modified Direction Feature (MDF) and its extended version: the Enhanced MDF (EMDF). Two neural network-based techniques and Support Vector Machines (SVMs) were investigated and compared for the process of signature verification. The classifiers were trained using genuine specimens and other randomly selected signatures taken from a publicly available of 3840 genuine signatures from 160 volunteers and 4800 targeted forged signatures. A distinguishing error rate (DER) of 17.78% was obtained with the SVM whilst keeping the false acceptance rate for random forgeries (FARR) below 0.16%
Citation
Proceedings of the Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), v.2, p. 734-738
ISBN
0769528228
ISSN
1520-5363
Link
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Title
Off-line Signature Verification Using Enhanced Modified Direction Features in Conjunction with Neural Classifiers and Support Vector Machines
Type of document
Conference Publication
Entity Type
Publication

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