Offline signature verification and skilled forgery detection using HMM and sum graph features with ANN and knowledge based classifier

26 February 2010 Offline signature verification and skilled forgery detection using HMM and sum graph features with ANN and knowledge based classifier

Mohit Mehta, Vijay Choudhary, Rupam Das, Ilyas Khan

Author Affiliations +

Proceedings Volume 7546, Second International Conference on Digital Image Processing; 75462G (2010) https://doi.org/10.1117/12.853308
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore

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.

Citation Download Citation

Mohit Mehta, Vijay Choudhary, Rupam Das, and Ilyas Khan "Offline signature verification and skilled forgery detection using HMM and sum graph features with ANN and knowledge based classifier", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75462G (26 February 2010); https://doi.org/10.1117/12.853308

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