Offline Signature Recognition

2011 ◽  
pp. 1431-1438
Author(s):  
Indrani Chakravarty
Keyword(s):  
Fingerprint Recognition ◽  
Signature Verification ◽  
Dynamic Features ◽  
Identity Verification ◽  
Behavioral Biometrics ◽  
Verification Methods ◽  
Protection Mechanisms ◽  
Signature Recognition ◽  
Online Methods ◽  
Related Identity

The most commonly used protection mechanisms today are based on either what a person possesses (e.g. an ID card) or what the person remembers (like passwords and PIN numbers). However, there is always a risk of passwords being cracked by unauthenticated users and ID cards being stolen, in addition to shortcomings like forgotten passwords and lost ID cards (Huang & Yan, 1997). To avoid such inconveniences, one may opt for the new methodology of Biometrics, which though expensive will be almost infallible as it uses some unique physiological and/or behavioral (Huang & Yan, 1997) characteristics possessed by an individual for identity verification. Examples include signature, iris, face, and fingerprint recognition based systems. The most widespread and legally accepted biometric among the ones mentioned, especially in the monetary transactions related identity verification areas is carried out through handwritten signatures, which belong to behavioral biometrics (Huang & Yan,1997). This technique, referred to as signature verification, can be classified into two broad categories - online and off-line. While online deals with both static (for example: number of black pixels, length and height of the signature) and dynamic features (such as acceleration and velocity of signing, pen tilt, pressure applied) for verification, the latter extracts and utilizes only the static features (Ramesh and Murty, 1999). Consequently, online is much more efficient in terms of accuracy of detection as well as time than off-line. But, since online methods are quite expensive to implement, and also because many other applications still require the use of off-line verification methods, the latter, though less effective, is still used in many institutions.

Online Signature Recognition

2011 ◽  
pp. 1456-1462
Author(s):  
Indrani Chakravarty
Keyword(s):  
The Other ◽  
Signature Verification ◽  
Daily Lives ◽  
Verification Method ◽  
Other Hand ◽  
Verification Methods ◽  
Online Signature ◽  
Signature Recognition ◽  
Online Signature Verification ◽  
Online Methods

Security is one of the major issues in today’s world and most of us have to deal with some sort of passwords in our daily lives; but, these passwords have some problems of their own. If one picks an easy-to-remember password, then it is most likely that somebody else may guess it. On the other hand, if one chooses too difficult a password, then he or she may have to write it somewhere (to avoid inconveniences due to forgotten passwords) which may again lead to security breaches. To prevent passwords being hacked, users are usually advised to keep changing their passwords frequently and are also asked not to keep them too trivial at the same time. All these inconveniences led to the birth of the biometric field. The verification of handwritten signature, which is a behavioral biometric, can be classified into off-line and online signature verification methods. Online signature verification, in general, gives a higher verification rate than off-line verification methods, because of its use of both static and dynamic features of the problem space in contrast to off-line which uses only the static features. Despite greater accuracy, online signature recognition is not that prevalent in comparison to other biometrics. The primary reasons are: • It cannot be used everywhere, especially where signatures have to be written in ink; e.g. on cheques, only off-line methods will work. • Unlike off-line verification methods, online methods require some extra and special hardware, e.g. electronic tablets, pressure sensitive signature pads, etc. For off-line verification method, on the other hand, we can do the data acquisition with optical scanners. • The hardware for online are expensive and have a fixed and short life cycle. In spite of all these inconveniences, the use online methods is on the rise and in the near future, unless a process requires particularly an off-line method to be used, the former will tend to be more and more popular.

Off-Line Signature Recognition

2011 ◽  
pp. 870-875
Author(s):  
Indrani Chakravarty ◽  
Nilesh Mishra ◽  
Mayank Vatsa ◽  
Richa Singh ◽  
P. Gupta
Keyword(s):  
Fingerprint Recognition ◽  
Identity Verification ◽  
Protection Mechanisms ◽  
Signature Recognition

The most commonly used protection mechanisms today are based on either what a person possesses (e.g. an ID card) or what the person remembers (like passwords and PIN numbers). However, there is always a risk of passwords being cracked by unauthenticated users and ID cards being stolen, in addition to shortcomings like forgotten passwords and lost ID cards (Huang & Yan, 1997). To avoid such inconveniences, one may opt for the new methodology of Biometrics, which though expensive will be almost infallible as it uses some unique physiological and/or behavioral (Huang & Yan, 1997) characteristics possessed by an individual for identity verification. Examples include signature, iris, face, and fingerprint recognition based systems.

Online Signature Recognition

2011 ◽  
pp. 885-890 ◽  
Author(s):  
Indrani Chakravarty ◽  
Nilesh Mishra ◽  
Mayank Vatsa ◽  
Richa Singh ◽  
P. Gupta
Keyword(s):  
The Other ◽  
Signature Verification ◽  
Daily Lives ◽  
Verification Method ◽  
Security Breaches ◽  
Other Hand ◽  
Verification Methods ◽  
Online Signature ◽  
Signature Recognition ◽  
Online Signature Verification

Security is one of the major issues in today’s world and most of us have to deal with some sort of passwords in our daily lives; but, these passwords have some problems of their own. If one picks an easy-to-remember password, then it is most likely that somebody else may guess it. On the other hand, if one chooses too difficult a password, then he or she may have to write it somewhere (to avoid inconveniences due to forgotten passwords) which may again lead to security breaches. To prevent passwords being hacked, users are usually advised to keep changing their passwords frequently and are also asked not to keep them too trivial at the same time. All these inconveniences led to the birth of the biometric field. The verification of handwritten signature, which is a behavioral biometric, can be classified into off-line and online signature verification methods. Online signature verification, in general, gives a higher verification rate than off-line verification methods, because of its use of both static and dynamic features of the problem space in contrast to off-line which uses only the static features. Despite greater accuracy, online signature recognition is not that prevalent in comparison to other biometrics. The primary reasons are: • It cannot be used everywhere, especially where signatures have to be written in ink; e.g. on cheques, only off-line methods will work. • Unlike off-line verification methods, online methods require some extra and special hardware, e.g. electronic tablets, pressure sensitive signature pads, etc. For off-line verification method, on the other hand, we can do the data acquisition with optical scanners. • The hardware for online are expensive and have a fixed and short life cycle.

Local Features Analysis of On-Line Signature Using Modified Distance of DTW

2015 ◽  
Vol 12 (03) ◽  
pp. 1550016 ◽  
Author(s):  
Saeid Rashidi ◽  
Ali Fallah ◽  
Farzad Towhidkhah
Keyword(s):  
Signature Verification ◽  
Classification Error ◽  
Dynamic Features ◽  
Discrimination Power ◽  
Discrimination Capability ◽  
Verification System ◽  
Signature Database ◽  
Acceptable Method ◽  
Functional Features ◽  
Dynamic Time

Signature verification is a reliable and publicly acceptable method for authentication. Each signature is represented as a set of functional features such as coordinates of signature points, pen pressure and pen angle and therefore many features are available to the designer of signature verification system. The efficiency of any signature verification system depends mainly on the discrimination power and robustness of the features use in the system. This paper evaluates 40 dynamic features viewpoint classification error and consistency for extracting the best subset once a set of features provide maximal discrimination capability between genuine and forgery signatures. A modified distance of dynamic time warping (DTW) algorithm is proposed to improve performance of verification phase. The proposed system is evaluated on the public SVC2004 signature database. The experimental results show that first, the most discriminate and consistent features are velocity-based. Second, average equal error rate (EER) for proposed algorithm in comparison with the general DTW algorithm show a 47.5% decrease. Moreover, comparative study based on different classifier with skilled forgery show that the best result has EER of 1.73% using Parzen window classifier.

scholarly journals On-line Signature Verification Based on GA-SVM

2015 ◽  
Vol 11 (6) ◽  
pp. 49 ◽  
Author(s):  
Dong Huang ◽  
Jian Gao
Keyword(s):  
Genetic Algorithm ◽  
Feature Subset Selection ◽  
Signature Verification ◽  
Support Vector ◽  
Svm Classifier ◽  
Support Vector Data Description ◽  
Feature Subset ◽  
Dynamic Features ◽  
On Line ◽  
One Class Classifier

With the development of pen-based mobile device, on-line signature verification is gradually becoming a kind of important biometrics verification. This thesis proposes a method of verification of on-line handwritten signatures using both Support Vector Data Description (SVM) and Genetic Algorithm (GA). A 27-parameter feature set including shape and dynamic features is extracted from the on-line signatures data. The genuine signatures of each subject are treated as target data to train the SVM classifier. As a kernel based one-class classifier, SVM can accurately describe the feature distribution of the genuine signatures and detect the forgeries. To improving the performance of the authentication method, genetic algorithm (GA) is used to optimise classifier parameters and feature subset selection. Signature data form the SVC2013 database is used to carry out verification experiments. The proposed method can achieve an average Equal Error Rate (EER) of 4.93% of the skill forgery database.

scholarly journals Analyzing the Effects of Data Augmentation on Single and Multimodal Biometrics

2020 ◽  
Vol 39 (3) ◽  
pp. 647-656
Author(s):  
Mehwish Leghari ◽  
Shahzad Memon ◽  
Lachman Das Dhomeja ◽  
Akhter Hussain Jalbani
Keyword(s):  
Data Augmentation ◽  
Research Study ◽  
Real Data ◽  
Fingerprint Recognition ◽  
Multimodal Biometrics ◽  
Biometric Data ◽  
Augmentation Techniques ◽  
Signature Recognition ◽  
Using Data ◽  
Level Fusion

Now-a-days, in the field of machine learning the data augmentation techniques are common in use, especially with deep neural networks, where a large amount of data is required to train the network. The effectiveness of the data augmentation technique has been analyzed for many applications; however, it has not been analyzed separately for the multimodal biometrics. This research analyzes the effects of data augmentation on single biometric data and multimodal biometric data. In this research, the features from two biometric modalities: fingerprint and signature, have been fused together at the feature level. The primary motivation for fusing biometric data at feature level is to secure the privacy of the user’s biometric data. The results that have been achieved by using data augmentation are presented in this research. The experimental results for the fingerprint recognition, signature recognition and the feature-level fusion of fingerprint with signature have been presented separately. The results show that the accuracy of the training classifier can be enhanced with data augmentation techniques when the size of real data samples is insufficient. This research study explores that how the effectiveness of data augmentation gradually increases with the number of templates for the fused biometric data by making the number of templates double each time until the classifier achieved the accuracy of 99%.

scholarly journals Time Independent Signature Verification using Normalized Weighted Coefficients

2016 ◽  
Vol 6 (6) ◽  
pp. 2658
Author(s):  
Manas Singhal ◽  
Manish Trikha ◽  
Maitreyee Dutta
Keyword(s):  
Time Factor ◽  
Signature Verification ◽  
Performance Parameters ◽  
Legal Documents ◽  
Individual Person ◽  
Offline Signature Verification ◽  
Verification Methods ◽  
Online Signature ◽  
Online Signature Verification ◽  
Debit Cards

<p>Signature verification is one of the most widely accepted verification methods in use. The application of handwritten signatures includes the banker’s checks, the credit and debit cards issued by banks and various legal documents. The time factor plays an important role in the framing of signature of an individual person. Signatures can be classified as: offline signature verification and online signature verification. In this paper a time independent signature verification using normalized weighted coefficients is presented. If the signature defining parameters are updated regularly according to the weighted coefficients, then the performance of the system can be increased to a significant level. Results show that by taking normalized weighted coefficients the performance parameters, FAR and FRR, can be improved significantly.</p>

scholarly journals A Combined Method for On-Line Signature Verification

2014 ◽  
Vol 14 (2) ◽  
pp. 92-97
Author(s):  
Desislava Boyadzhieva ◽  
Georgi Gluhchev
Keyword(s):  
Feature Extraction ◽  
Recognition System ◽  
Signature Verification ◽  
System Evaluation ◽  
Combined Method ◽  
Average Accuracy ◽  
Feature Extraction And Selection ◽  
On Line ◽  
Signature Recognition

Abstract A combined method for on-line signature verification is presented in this paper. Moreover, all the necessary steps in developing a signature recognition system are described: signature data pre-processing, feature extraction and selection, verification and system evaluation. NNs are used for verification. The influence of the signature forgery type (random and skilled) over the verification results is investigated as well. The experiments are carried out on SUsig database which consists of genuine and forgery signatures of 89 users. The average accuracy is 98.46%.

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