Signature verification based on a feature extraction technique

Saba Mushtaq

ISBN 10: 366854154X / ISBN 13: 9783668541542

Published by GRIN Verlag, 2017

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Master's Thesis from the year 2012 in the subject Technology, grade: 10, , language: English, abstract: 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.

Seller Inventory # 9783668541542

Bibliographic Details

• Title: Signature verification based on a feature extraction technique
• Publisher: GRIN Verlag
• Publication Date: 2017
• Language: English
• ISBN 10: 366854154X
• ISBN 13: 9783668541542
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 5D millimeters 210 millimeters width by 148 millimeters height by 5 millimeters depth
• Weight: 107 grams

About this title

Synopsis

Master's Thesis from the year 2012 in the subject Technology, grade: 10, language: English, abstract: 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.