Persian Writer Identification Using Extended Gabor Filter
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Towards robust writer verification by correcting unnatural slant
Pattern Recognition Letters
Robustness of biometric gait authentication against impersonation attack
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part I
| Hi-index | 0.00 |
We investigated the detection of handwriting forgery by both human and machine. We obtained experimental handwriting data from subjects writing samples in their natural style and writing forgeries of other subjects' handwriting. These handwriting samples were digitallyscanned and stored in an image database. We investigated the ease of forging handwriting, and found that many subjects can successfully forge the handwriting of others in terms of shape and size by tracing the authentic handwriting. Our hypothesis is that the authentic handwriting samples provided by subjects in their own natural writing style will have smooth ink traces, while forged handwritings will have wrinkly traces. We believe the reason for this is that forged handwriting is often either traced or copied slowly and is therefore more likely toappear wrinkly when scanned with a high-resolution scanner. Using seven handwriting distance features, we trained an artificial neural network to achieved 89% accuracy on test samples.