Online handwritten signature verification system based on DWT features extraction and neural network classification

Handwritten signature is the most widely accepted biometric to identity verification. The target of research is to present online handwritten signature verification system based on discrete wavelet transform (DWT) features extraction and feed forward back propagation error neural network recognition. Steps for verifying online handwritten signature in this system start with extracting pen position data (x and y positions) of points that forming the signature. Pen-movement angles are then derived from pen position data. To reduce variations in pen-position and pen-movement angles dimensionality, data are normalized and resampled. To enhance the difference between a genuine signature and its forgery, the signature is verified in DWT domain. Low frequency sub-band signals (approximations) of pen-position parameter and pen-movement angle parameter are considered as intrapersonal features. These are used for suppressing variations between different genuine signatures and enhancing the interpersonal variations, hence are given higher scores within total recognition process. Both of pen-position and pen-movement angle features are then associated for obtaining a decision about online handwritten signature verification. A multi-matcher consists of six neural networks which use multiple representations and matching for the same input biometric signal is used to verify signature. The recognition rate for each of these neural network recognizers is discussed and a comparison of those rates is performed. Experiments are carried on signature database for five users each of 20 genuine and 20 skilled forgery signatures. Recognition success rate for genuine signatures is 95%.