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.
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