There are two kinds of pattern recognition. They are statistical pattern recognition and structural pattern recognition only vectors are taken into account for statistical pattern recognition and they are used to perform tasks.
List Of Acronyms illustration not visible in this excerpt Acknowledgement With exception, I would like to express my sincere gratitude to the Almighty Allah who is full of mercy and compassion for giving me strength and good health during the whole period of my study.
I wish to extend sincere thanks to my supervisor Dr. Ajaz Hussain Mir H. D Electronics and Communication for his time, support, suggestions ,criticism and ideas that shaped this work.
Special thanks goes to Mrs. Farida Khurshid for her invaluable advice as a friend and a mentor. I am highly indebted to my family for their love, blessings, support and encouragement during the days of research.
Lastly I thank my friends and not forgetting my course mates for their openness and availability to discuss diverse social and academic issues, some of whom contributed to this study by providing constructive criticism and sample signatures. 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 genuine signatures and 30 forged signatures consisting of a training set and a test set are used.
Chapter 1 Introduction 1. Introduction Information security is concerned with the assurance of confidentiality, integrity and availability of information in all forms.
There are many tools and techniques that can support the management of information security. But system based on biometric has evolved to support some aspects of information security Bhattacharyya et al.
Biometric authentication supports the facet of identification, authentication and non-repudiation in information security.
Biometric authentication has grown in popularity as a way to provide personal identification. Individual passwords, pin identification or even token based arrangement all have deficiencies that restrict their applicability in a widely-networked society.
Biometric is used to identify the identity of an input sample when compared to a template, used in cases to identify specific people by certain characteristics. Standard validation systems often use multiple inputs of samples for sufficient validation, such as particular characteristics of the sample.
This intends to enhance security as multiple different samples are required such as security tags and codes and sample dimensions. So, the advantage claimed by biometric authentication is that they can establish an unbreakable one-to-one correspondence between an individual and a piece of data 1.
Our motivation behind this project is to implement a simple texture analysis approach for such handwritten signature verification avoiding all such complexities of handling a huge database of monochrome pictures corresponding to signatures of each individual.
Here to avoid complex image processing methods like thinning, scaling and other morphological schemes, the signatures taken in the form of monochrome tiff images are firstly converted into 2D data arrays.
Then, texture features are calculated. Statistical formula for Mean, standard deviation are being followed. The Recognition scheme is based on texture Analysis of signature images. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user, and not anyone else.
Signatures are composed of special characters and flourishes and therefore most of the time they can be unreadable. Also intrapersonal variations and the differences make it necessary to analyze them as complete images and not as letters and words put together.
As signatures are the primary mechanism both for authentication and authorization in legal transactions, the need for research in efficient automated solutions for signature recognition and verification has increased in recent years. Various methods have already been introduced in this field but by far the texture method for feature extraction we have used has not been used for signatures till now.
The first, which is the older and is used in biological studies, including forestry, is the collection, synthesis, analysis and management of quantitative data on biological communities such as forests.
Biometrics in reference to biological sciences has been studied and applied for several generations and is somewhat simply viewed as "biological statistics". Authentication is the act of establishing or confirming something or someone as authentic, that is, that claims made by or about the thing are true.
A short overview in this field can be divided into three parts and they are Past, Present and Future Bhattacharyya et al.
Chinese merchants used ink to take children's fingerprints for identification purposes. InAlphonse Bertillon studied body mechanics and measurements to help in identifying criminals. The police used his method, the Bertillonage method, until it falsely identified some subjects.Thesis (PhD (Mathematical Sciences))--University of Stellenbosch, Thesis A great deal of work has been done in the area of off-line signature verification over the past two decades.
1 online signature verification techniques a thesis submitted in partial fulfillment of the requirements for the degree of master of technology. offline signature verification with user-based and global classifiers of local features mustafa berkay yilmaz cs, ph.d.
thesis, thesis advisor: berrin yanikoglu˘. Thesis on Image Processing consists promising topic for research scholars for Interpolations a concept in image processing is used to display reasonable images in many resolutions.
Image processing checks the image for unnecessary features and eliminates them in order to minimize the information.
Master thesis: Signature verification in consignment notes iii Preface A large amount of time and effort is spent in realizing this thesis. The work that led to the current state of the thesis could not be realized without the help of certain people. In this section I would like to thank those persons.
3 DECLARATION This is to certify that the work in this Thesis Report entitled “Offline Signature Verification Scheme” by Prabit Kumar Mishra and Mukti Ranjan Sahoo has been carried out under my supervision in partial.