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Signature verification based on a feature extraction technique

Master's Thesis 2012 59 Pages

Technology

Summary

In this research we evaluate the use of GLRLM features in offline handwritten signature verification. For each known writer we take a sample of fifteen genuine signatures and extract their GLRLM descriptors. We also used some forged signatures to test the efficiency of our system.

We calculate the simple statistical measures and also inter- and intra-class Euclidean distances (measure of variability within the same author) among GLRLM descriptors of the known signatures. The key points Euclidean distances, the image distances and the intra class thresholds are stored as templates.

We evaluate use of various intra-class distance thresholds like the mean, standard deviation and range. For each signature claimed to be of the known writers, we extract its GLRLM descriptors and calculate the inter-class distances, that is the Euclidean distances between each of its GLRLM descriptors and those of the known template and image distances between the test signature and members of the genuine sample. The intra-class threshold is compared to the inter-class threshold for the claimed signature to be considered a forgery. A database of 525 genuine signatures and 30 forged signatures consisting of a training set and a test set are used.

Details

Pages
59
Year
2012
ISBN (eBook)
9783668541535
ISBN (Book)
9783668541542
File size
972 KB
Language
English
Catalog Number
v376136
Grade
10
Tags
signature Offline signature verification texture based verification Handwritten signatures

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Title: Signature verification based on a feature extraction technique