DIGITAL SIGNATURE VERIFICATION SYSTEM
Abstract
For effective identification of a specify person, signatures have proven to be a significant biometric. The signature of an individual is a significant biometric attribute of a person which can be used to authenticate human identity. Furthermore, individual signatures can be dealt as a picture and recognized using a computer software. With the evolution of modern computers, there is need to develop fast algorithms for signature recognition. They are different ways to deal with signature acknowledgment with a lot of scope of research. In this paper, signature verification and is considered as a means to fight signature forgery, where the signature is captured and verified by the system in an image format. Signatures are verified dependent on parameters recovered from the signature using various image processing techniques. This paper presents a case study on signature verification method for verifying signatures using a digital verification system. The propose system starts with a step called “signature preprocessing” that aims to enhance the appearance of the signature image and get it ready for the feature extraction stage that follows. In the feature extraction process, Discrete Radon Transfer is used to build a vector for each signature image (DRT). For the purpose of determining the best angles and the quantity of angles employed in DRT, various sets of angles (4, 6, 8, 10) are used. 32 and 64 are taken from the feature vector. Euclidean distance and Probabilistic Neural Network are two methods used for verification (PNNs).
DIGITAL SIGNATURE VERIFICATION SYSTEM
CHAPTER ONE
General Introduction
1.1 Introduction
Signatures are composed of special character and flourishes and therefore most of the time they can be unreadable. Also, intrapersonal variations and interpersonal differences make it necessary to analyze them as complete images but not as letters and words put together. Signatures have been the primary mechanism both for authentication and authorization in legal documentation in recent years.
Based on different applications, signature verification system can be operated in two different modes (Plamondon and Srihari, 2000, Seiler et al., 1996); online and offline mode. In the online mode, the signature verification is dealing with the instant inputs from the system such as credit card verifier. For offline mode, the verification is done on the recorded signatures such as bank’s document verification (Dimauro et al., 1997).
Generally, signature verification system can be categorised into two types: dynamic and static. The dynamic signature verification system is dealing with signal processing while the static signature verification system is more on image processing. Some techniques applied in static signature verification systems are neural networks (Bajaj and Chaudhury, 1997; Huang and Yan, 1997; Karouni et al., 2011), model based approaches (Huang and Yan, 2002; Wen et al., 2009) and wavelets transform (Deng et al., 1997) . Meanwhile, Dynamic Time Warping (DTW) (Fenton et al., 2006) and Gaussian Mixture Modelling (GMM) methods (Miguel-Hurtado et al., 2007, 2008) have been introduced for dynamic automated signature verification system. Basically, DTW is used in pre-processing to remove the intrinsic variability from user signature by aligning the acquired signal. GMM is used to model the probabilistic distribution of the set of pseudo-distances and to calculate the likelihood ratio between the sample and reference signature.
There are other approaches which based on the concept of filters (Tanaka and Bargiela, 2005). Firstly, global features of the signature, such as average velocity are considered through Euclidian distance. In the second filter, local features are considered. Strokes are segmented using the minima of the velocity and encoded before comparing them using DTW (Miguel-Hurtado et al., 2007, 2008) and signer -specific thresholds. On the other hand, Linear Prediction Coding (LPC) cestrum and Neural Networks (Wu et al., 1997) are proposed in the dynamic signature verification system. LPC is used in the pre-processing stage and its coefficients are used as the input to the neural networks. The neural networks (Mailah and Han, 2008) mostly used in the verification process. Besides that, performance could be improved by fusing static and dynamic signature verification techniques (Alonso-Fernandez et al., 2009).
However, two of the major challenges faced in signature verification are intra-class variability where the individual has slight variations in their own signature writing styles over a period of time, and inter-class variability where some other person tries to mimic or simulate the signature of an individual to get an illicit access through a signature verification system. Traditionally, it has been thought that these sources of variability, specially the intra-class variabil-ity, is much higher in mobile scenarios compared to desktop digitizing tablets for signature veri cation, which results in degraded veri cation performance in mobile scenarios.
1.2 Problem Statement
Signature verification techniques utilize many different characteristics of an individual’s signature in order to authenticate that individual ( Vacca, 2007). The advantages of using such an authentication technique are; (i) signatures are widely accepted by society as a form of verification ( Kung et al., 2004), (ii) information required is not sensitive and (iii) forging of an individual’s signature does not mean a long-life loss of that individual’s identity. The general idea of this re-search is to investigate a signature verification technique which is not costly to built, user friendly in terms of configuration, robust against imposters and is reliable even if the individual is under different emotions.
In signature verification application, the signatures are processed to extract features that are later fed into a classifier. The task of the classifier is to assign the signature features to classes of individuals. The selection of signature features is critical in determining the performance of a signature verification system. In this research, the features were selected according to certain criterions. Mainly, the features have to be small enough to be stored in a smart card and does not require complex classification techniques.
There are two ways of validating a signature. They are static and dynamic. Static features are comprised of features which are extracted from signatures that are recorded as an image whereas dynamic features are extracted from signatures that are acquired in real-time ( Faundez-Zanuy, 2005; Plamondon and Srihari, 2000). These feature types can be broken down into two types which are function based and parameter based features.
The function based features describes a signature in terms of a time-function. Examples of function based features include position, pressure and velocity (Di-mauro et al., 2004). While the performance of such features is well known to researchers in accurately verifying signatures, they are not suitable in this case due to the complexity of its matching algorithm. Hence, the use of parameter based features is more appropriate.
Signature verification applications are used in our daily lives and will be exposed to human emotions. The system has to be reliable in accurately verifying an individual’s signature even if he/she is under different emotions. Sackheim ( Sackheim, 1990; Gardner, 2002; Lange et al., 2006 and Yank, 1991) have shown that handwriting of a person is affected by their emotions. Most of the techniques which have been proposed by researchers have not been tested against people’s emotions.
1.3 Research Aim and Objectives
The primary objective of this research is to design a digital signature verification system. Other objectives include:
- Choose a suitable features required for a robust signature verification tech-nique, yet inexpensive to build and user friendly in terms of configuration.
- Investigate the performance of selected classifiers which are suitable for classifying the chosen features.
1.4 Scope of the Study
The goal of this project is to develop a digital signature verification system that can distinguish between authentic signatures and forgeries, as well as be able to spot forgeries and, at the same time, decrease the rejection of real signatures.
In order to extract these distinctive traits, the proposed system uses DRT. The tested signature can be recognised as authentic or forged after the system has been trained using PNN.
1.5 Organization of Chapters
Chapter one covers the introductory part of the research.
In chapter two, the components of the biometric system and the related technologies are introduced. Additionally, a brief explanation of the fundamental duties performed by the signature biometric is provided.
The processes of the proposed signature verification system’s design and implementation are covered in Chapter three. To put it simply, it consists of the m methods utilized in the preprocessing, feature extraction, and matching stages.
The outcomes of the suggested system’s results analysis and performance evaluation of the signature dataset collecting are presented in Chapter four.
The primary findings of this study are presented in Chapter five, which also includes a list of recommendations for additional research.
DIGITAL SIGNATURE VERIFICATION SYSTEM