Handwritten Signature Verification (Offline) Using Neural Network Approaches: A Comparative Study
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 and Associative Memory Net (AMN) has been conducted. To decrease the time complexity a corresponding parallel version using Open MP 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.