On‐line handwritten signature verification using stroke direction coding

1996 ◽  
Vol 35 (9) ◽  
pp. 2526 ◽  
Author(s):  
Ramanujan S. Kashi
Keyword(s):  
Signature Verification ◽  
On Line ◽  
Handwritten Signature

Smartphone-based Handwritten Signature Verification using Acoustic Signals

2021 ◽  
Vol 5 (ISS) ◽  
pp. 1-26
Author(s):  
Run Zhao ◽  
Professor Dong Wang ◽  
Qian Zhang ◽  
Xueyi Jin ◽  
Ke Liu
Keyword(s):  
Information Exchange ◽  
Characteristic Curve ◽  
Acoustic Signals ◽  
Signature Verification ◽  
Special Device ◽  
Dominant Hand ◽  
Siamese Network ◽  
On Line ◽  
Handwritten Signature ◽  
Verification Techniques

Handwritten signature verification techniques, which can facilitate user authentication and enable secure information exchange, are still important in property safety. However, on-line automatic handwritten signature verification usually requires dynamic handwritten patterns captured by a special device, such as a sensor-instrumented pen, a tablet or a smartwatch on the dominant hand. This paper presents SonarSign, an on-line handwritten signature verification system based on inaudible acoustic signals. The key insight is to use acoustic signals to capture the dynamic handwritten signature patterns for verification. Particularly, SonarSign exploits the built-in speakers and microphones of smartphones to transmit a specially designed training sequence and record the corresponding echo for channel impulse response (CIR) estimation, respectively. Based on the sensitivity of CIR to the tiny surrounding environment changes including handwritten signature actions, SonarSign designs an attentional multi-modal Siamese network for end-to-end signatures verification. First, multi-modal CIR streams are fused to extract representative signature pattern features from spatio-temporal dimensions. Then an attentional Siamese network is elaborated to verify whether the given two signatures are from the same signatory. Extensive experiments in real-world scenarios show that SonarSign can achieve accurate and robust signatures verification with an AUC (Area Under ROC (Receiver Operating Characteristic) Curve) of 98.02% and an EER (Equal Error Rate) of 5.79% for unseen users.

Based on EADTW On-Line Handwriting Signature Handwriting Signature Verification System Design and Implementation

2014 ◽  
Vol 556-562 ◽  
pp. 5902-5905 ◽  
Author(s):  
Lei Ding ◽  
Yong Jun Luo ◽  
Yang Yang Wang ◽  
Zheng Li ◽  
Bing Yin Yao
Keyword(s):  
System Design ◽  
Dynamic Time Warping ◽  
Test Sample ◽  
Signature Verification ◽  
Time Warping ◽  
Design And Implementation ◽  
On Line ◽  
Dynamic Time ◽  
Handwritten Signature ◽  
New System

In view of poor accuracy and slow calculation of the traditional on-line handwriting signature verification, an on-line handwriting signature verification based on Early Abandon Dynamic Time Warping (EADTW) was designed and implemented after numerous researched. The training template followed the mechanism of benchmark signature, while the certification part adopted EADTW algorithm. The experimental results showed that compared with on-line handwritten signature system based on DTW (dynamic time warping), this new system not only greatly reduced cumbersome and repeated calculation, but also obviously improved the accuracy, The bigger the test sample is, the more obvious the advantage is.

SVM-DSmT COMBINATION FOR THE SIMULTANEOUS VERIFICATION OF OFF-LINE AND ON-LINE HANDWRITTEN SIGNATURES

2012 ◽  
Vol 11 (03) ◽  
pp. 1250018 ◽  
Author(s):  
NASSIM ABBAS ◽  
YOUCEF CHIBANI
Keyword(s):  
Function Theory ◽  
Belief Function ◽  
Signature Verification ◽  
Support Vector ◽  
Verification System ◽  
Belief Function Theory ◽  
On Line ◽  
Handwritten Signature ◽  
Belief Models ◽  
False Acceptance

A combination handwritten signature verification system is proposed for managing conflicts provided from each individual off-line and on-line support vector machine (SVM), respectively. Basically, the system is divided into three parts: (i) Off-line verification system, (ii) on-line verification system and (iii) combination module using belief function theory. The proposed framework allows combining the normalized SVM outputs and uses an estimation technique based on the dissonant model of Appriou to compute the belief assignments. Combination is performed using belief models such as Dempster-Shafer (DS) rule and proportional conflict redistribution (PCR) rule followed by the likelihood ratio-based decision making. Experiments are conducted on the well-known NISDCC signature collection using false rejection and false acceptance criteria. The obtained results show that the proposed combination framework using Dezert-Smarandache (DSm) theory yields the best verification accuracy even when individual off-line and on-line classifications provide conflicting results.

scholarly journals Signature verification: A comprehensive study of the hidden signature method

2015 ◽  
Vol 25 (3) ◽  
pp. 659-674 ◽  
Author(s):  
Joanna Putz-Leszczyńska
Keyword(s):  
Error Rates ◽  
Estimation Methods ◽  
Signature Verification ◽  
Traditional Use ◽  
Line System ◽  
Comprehensive Comparison ◽  
On Line ◽  
Dynamic Time ◽  
The Right ◽  
Handwritten Signature

Abstract Many handwritten signature verification algorithms have been developed in order to distinguish between genuine signatures and forgeries. An important group of these methods is based on dynamic time warping (DTW). Traditional use of DTW for signature verification consists in forming a misalignment score between the verified signature and a set of template signatures. The right selection of template signatures has a big impact on that verification. In this article, we describe our proposition for replacing the template signatures with the hidden signature-an artificial signature which is created by minimizing the mean misalignment between itself and the signatures from the enrollment set. We present a few hidden signature estimation methods together with their comprehensive comparison. The hidden signature opens a number of new possibilities for signature analysis. We apply statistical properties of the hidden signature to normalize the error signal of the verified signature and to use the misalignment on the normalized errors as a verification basis. A result, we achieve satisfying error rates that allow creating an on-line system, ready for operating in a real-world environment

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