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.
Table Of Contents.
List Of Figures
List Of Tables.
List Of Acronyms
Chapter 1 Introduction
1. Introduction
1.2 Problem Motivation
1.3 Biometrics Introduction
1.3.1 Past
1.3.2 Present
1.3.3 Future
1.4 Personal Biometric Criteria
1.5 Biometric System-Level Criteria
1.6 Performance parameters
1.7 Thesis outline
CHAPTER 2 Introduction to Signature Verification
2. Introduction
2.1 Pattern recognition
2.2 Feature extraction
2.3 Handwritten signatures
2.3.1 On-line and off-line signatures
2.4 Forgery types
2.4.1 Random forgeries
2.4.2 Simple forgeries
2.4.3 Skilled forgeries
2.5 Writer-dependent and writer-independent verification
2.6 Objectives
Chapter 3 Literature survey
3.1 Texture Analysis
3.1.1Inspection
3.1.2 Medical Image Analysis
3.2 Signature verification
3.3 Gray level run length encoding
Chapter 4 Problem Definition and Methodology
4. Introduction
4.1 Problem Definition
4.2 Steps Involved
4.2.1Signature enrolment
4.2.2 Obtaining region of interest
4.2.3 Feature extraction
4.2.4 Definition of the Run-Length Matrices
4.2.5 Calculation of Euclidean Distances
4.2.6 Creation of the known signature template.
4.2.7 Signature Verification
4.3 Measurement of the Signature Verifier Accuracy
4.4 Proposed Algorithm using Euclidean distance
Chapter 5 Experiment and Results
5. Introduction The simple statistical approach :
5.1.1 Examples of verified signatures:
5.2 Euclidean distance model
5.2.1 Threshold
Chapter 6 Conclusion
6.1 Conclusion
Chapter 7 Future work
7.1 Future work
References
Figure References
- Citation du texte
- Saba Mushtaq (Auteur), 2012, Signature verification based on a feature extraction technique, Munich, GRIN Verlag, https://www.grin.com/document/376136
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