Offline signature verification using intelligent algorithm

Abstract

Signature verification is important in banking, legal, financial transactions for security purpose. Offline signature verification is a complex task because active information i.e. temporal information is missing in static image. There is no standard feature extraction method for offline signature identification as in case of other behavior modalities e.g. in automatic speech recognition like LPCC (Linear Predictive Ceptral Coefficients).Our research presents an intelligent algorithm for feature extraction based on image difference of genuine signature image and questioned signature image. Six features i.e. average object area, entropy, standard deviation, mean, Euler no., and area are analyzed. Best results are reported using combination of Average Object Area, Mean, Euler No. and Area. CEDAR (Center of Excellence for Document Analysis) database is used for offline signature verification. The database consists of static signature samples taken from 55 users. The Proposed algorithm is quite efficient as it is less computationally. Experiments are performed with both models i.e. Writer-Independent (WI) system and Writer-Dependent.