scholarly journals A Review - Signature Verification System Using Deep Learning: A Challenging Problem

2021 ◽  
pp. 295-298
Author(s):  
Punam R. Patil ◽  
Bhushan V. Patil
Keyword(s):  
Deep Learning ◽  
Signature Verification ◽  
Educational Institutions ◽  
Business Organizations ◽  
Verification Method ◽  
Offline Signature Verification ◽  
Online Signature ◽  
On Line ◽  
Online Signature Verification ◽  
Verification Process

One of the challenging and effective way of identifying person through biometric techniques is Signature verification as compared to traditional handcrafted system, where a forger has access and also attempt to imitate it which is used in commercial scenarios, like bank check payment, business organizations, educational institutions, government sectors, health care industry etc. so the signature verification process is used for human examination of a single known sample. There are mainly two types of signature verification: static and dynamic. i) Static or off-line verification is the process of verifying an electronic or document signature after it has been made, ii) Dynamic or on-line verification takes place as a person creates his/her signature on a digital tablet or a similar device. As compared, Offline signature verification is not efficient and slow for a large number of documents. Therefore although vast and extensive research on signature verification there is need to more focus and review on the online signature verification method to increase efficiency using deep learning.

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>

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.

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.

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 An Offline Signature Verification and Forgery Detection Method Based on a Single Known Sample and an Explainable Deep Learning Approach

2020 ◽  
Vol 10 (11) ◽  
pp. 3716 ◽  
Author(s):  
Hsin-Hsiung Kao ◽  
Che-Yen Wen
Keyword(s):  
Deep Learning ◽  
Reference Sample ◽  
Rejection Rate ◽  
Personal Identification ◽  
Signature Verification ◽  
Verification Method ◽  
Testing Dataset ◽  
Verification Process ◽  
Local Feature Extraction ◽  
Handwritten Signature

Signature verification is one of the biometric techniques frequently used for personal identification. In many commercial scenarios, such as bank check payment, the signature verification process is based on human examination of a single known sample. Although there is extensive research on automatic signature verification, yet few attempts have been made to perform the verification based on a single reference sample. In this paper, we propose an off-line handwritten signature verification method based on an explainable deep learning method (deep convolutional neural network, DCNN) and unique local feature extraction approach. We use the open-source dataset, Document Analysis and Recognition (ICDAR) 2011 SigComp, to train our system and verify a questioned signature as genuine or a forgery. All samples used in our testing process are collected from a new author whose signatures are not present in the training or other stages. From the experimental results, we get the accuracy between 94.37% and 99.96%, false rejection rate (FRR) between 5.88% and 0%, false acceptance rate (FAR) between 0.22% and 5.34% in our testing dataset.

OSVFuseNet: Online Signature Verification by feature fusion and depth-wise separable convolution based deep learning

2020 ◽  
Vol 409 ◽  
pp. 157-172
Author(s):  
Chandra Sekhar Vorugunti ◽  
Viswanath Pulabaigari ◽  
Rama Krishna Sai Subrahmanyam Gorthi ◽  
Prerana Mukherjee
Keyword(s):  
Deep Learning ◽  
Feature Fusion ◽  
Signature Verification ◽  
Online Signature ◽  
Online Signature Verification
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