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Proceedings Paper

Offline signature verification and skilled forgery detection using HMM and sum graph features with ANN and knowledge based classifier
Author(s): Mohit Mehta; Vijay Choudhary; Rupam Das; Ilyas Khan
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Paper Abstract

Signature verification is one of the most widely researched areas in document analysis and signature biometric. Various methodologies have been proposed in this area for accurate signature verification and forgery detection. In this paper we propose a unique two stage model of detecting skilled forgery in the signature by combining two feature types namely Sum graph and HMM model for signature generation and classify them with knowledge based classifier and probability neural network. We proposed a unique technique of using HMM as feature rather than a classifier as being widely proposed by most of the authors in signature recognition. Results show a higher false rejection than false acceptance rate. The system detects forgeries with an accuracy of 80% and can detect the signatures with 91% accuracy. The two stage model can be used in realistic signature biometric applications like the banking applications where there is a need to detect the authenticity of the signature before processing documents like checks.

Paper Details

Date Published: 26 February 2010
PDF: 6 pages
Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75462G (26 February 2010); doi: 10.1117/12.853308
Show Author Affiliations
Mohit Mehta, Technocrats Institute of Technology (India)
Vijay Choudhary, Technocrats Institute of Technology (India)
Rupam Das, Integrated Ideas (India)
Ilyas Khan, Technocrats Institute of Technology (India)

Published in SPIE Proceedings Vol. 7546:
Second International Conference on Digital Image Processing
Kamaruzaman Jusoff; Yi Xie, Editor(s)

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