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 K-Nearest Neighbour and Dynamic Time Warping for Online Signature Verification

2021 ◽  
Author(s):  
Mohammad Saleem ◽  
BenceKovari
Keyword(s):  
Error Rate ◽  
Dynamic Time Warping ◽  
Signature Verification ◽  
Nearest Neighbour ◽  
Time Warping ◽  
Verification System ◽  
Online Signature ◽  
Online Signature Verification ◽  
Dynamic Time ◽  
Verification Systems

Online signatures are one of the most commonly used biometrics. Several verification systems and public databases were presented in this field. This paper presents a combination of knearest neighbor and dynamic time warping algorithms as a verification system using the recently published DeepSignDB database. Our algorithm was applied on both finger and stylus input signatures which represent both office and mobile scenarios. The system was first tested on the development set of the database. It achieved an error rate of 6.04% for the stylus input signatures, 5.20% for the finger input signatures, and 6.00% for a combination of both types. The system was also applied to the evaluation set of the database and achieved very promising results, especially for finger input signatures.

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.

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