International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 57 - Number 7 |
Year of Publication: 2012 |
Authors: Tirtharaj Dash, Tanistha Nayak, Subhagata Chattopadhyay |
10.5120/9128-3295 |
Tirtharaj Dash, Tanistha Nayak, Subhagata Chattopadhyay . Handwritten Signature Verification (Offline) using Neural Network Approaches: A Comparative Study. International Journal of Computer Applications. 57, 7 ( November 2012), 33-41. DOI=10.5120/9128-3295
Forgery detection has been a challenging area in the field of biometry, e. g. , handwritten signatures. Signature verification is a bi-objective optimization problem. The two crucial parameters are accuracy and time of computation. In this work, a comprehensive study on application of Adaptive Resonance Theory (ART) Nets (Type 1 and 2) and Associative Memory Net (AMN) has been conducted. To decrease the time complexity a corresponding parallel version using OpenMP is developed for each algorithm. The algorithms are trained with the original/genuine signature and tested with a sample of twelve very similar-looking forged signatures. The study concludes that ART-1 detects fake signatures with an accuracy of 99. 89%; whereas, ART-2 and AMN detect forgery with accuracies of 99. 99% and 75. 68% respectively which are comparable to other methods cited in this paper.