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.

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>

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>

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 VERIFICATION BY COMBINING SHAPE CONTEXTS AND LOCAL FEATURES

2006 ◽  
Vol 06 (03) ◽  
pp. 407-420 ◽  
Author(s):  
BIN LI ◽  
DAVID ZHANG ◽  
KUANQUAN WANG
Keyword(s):  
Spatial Information ◽  
Local Features ◽  
Local Feature ◽  
Signature Verification ◽  
Shape Context ◽  
Signature Database ◽  
Verification Methods ◽  
Online Signature ◽  
Online Signature Verification ◽  
Dynamic Time

Most parameter-based online signature verification methods achieve correspondence between the points of two signatures by minimizing the accumulation of their local feature distances. The matching based on minimizing the local feature distances alone is not adequate since the point contains not only local features but the distribution of the remaining points relative to it. One useful way to get correspondences between points on two shapes and measure the similarity of the two shapes is to use the shape context, since this descriptor can be used to describe the distributive relationship between a reference point and the remaining points on a shape. In this paper, we introduce a shape context descriptor for describing an online signature point which contains both 2D spatial information and a time stamp. A common algorithm, dynamic time warp (DTW), is used for the elastic matching between two signatures. When combining shape contexts and local features, we achieve better results than when using only the local features. We evaluate the proposed method on a signature database from the First International Signature Verification Competition (SVC2004). Experimental results demonstrate that the shape context is a good feature and has available complementarity for describing the signature point. The best result by combining the shape contexts and the local features yields an Equal Error Rate (EER) of 6.77% for five references.

scholarly journals Online signature verification using signature down-sampling and signer-dependent sampling frequency

2021 ◽  
Author(s):  
Mohammad Saleem ◽  
Bence Kovari
Keyword(s):  
Sampling Rate ◽  
Sampling Frequency ◽  
Signature Verification ◽  
Point Count ◽  
Digital Devices ◽  
Verification System ◽  
Verification Methods ◽  
Online Signature ◽  
Online Signature Verification ◽  
Verification Systems

AbstractOnline signature verification considers signatures as time sequences of different measurements of the signing instrument. These signals are captured on digital devices and therefore consist of a discrete number of samples. To enrich or simplify this information, several verifiers employ resampling and interpolation as a preprocessing step to improve their results; however, their design decisions may be difficult to generalize. This study investigates the direct effect of the sampling rate of the input signals on the accuracy of online signature verification systems without using interpolation techniques and proposes a novel online signature verification system based on a signer-dependent sampling frequency. Twenty verifier configurations were created for five different public signature databases and a variety of popular preprocessing approaches and evaluated for 20–40 different sampling rates. Our results show that there is an optimal range for the sampling frequency and the number of sample points that minimizes the error rate of a verifier. A sampling frequency range of 15–50 Hz and a signature point count of 60–240 provided the best accuracies in our experiments. As expected, lower ranges showed inaccurate results; interestingly, however, higher frequencies often decreased the verification accuracy. The results show that one can achieve better or at least the same verification accuracies faster by down-sampling the online signatures before further processing. The proposed system achieved competitive results to state-of-the-art systems for different databases by using the optimal sampling frequency. We also studied the effect of choosing individual sampling frequencies for each signer and proposed a signature verification system based on signer-dependent sampling frequency. The proposed system was tested using 500 different verification methods and improved the accuracy in 92% of the test cases compared to the usage of the original frequency.

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.

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