scholarly journals Online Signature Verification: State of the art

2005 ◽  
Vol 4 (2) ◽  
pp. 664-678 ◽  
Author(s):  
Ibrahim El-Henawy ◽  
Magdy Rashad ◽  
Omima Nomir ◽  
Kareem Ahmed
Keyword(s):  
State Of The Art ◽  
User Authentication ◽  
Cost Effective ◽  
The State ◽  
Signature Verification ◽  
Stroke Length ◽  
Online Signature ◽  
Online Signature Verification ◽  
Dynamic Signature ◽  
The Relationship

online or Dynamic signature verification (DSV) is one of the most acceptable, intuitive, fast and cost effective tool for user authentication. DSV uses some dynamics like speed, pressure, directions, stroke length and pen-ups/pen-downs to verify the signer's identity. The state of the art in DSV is presented in this paper. several approaches for DSV are compared and the most influential techniques in this field are highlighted. We concentrate on the relationship between the verification approach used (the nature of the classifier) and the type of features that are used to represent the signature.

Online Signature Verification

2014 ◽  
Vol 24 ◽  
pp. 5-19 ◽  
Author(s):  
Marianela Parodi ◽  
Juan C. Gomez ◽  
Linda Alewijnse ◽  
Marcus Liwicki
Keyword(s):  
Information Fusion ◽  
Wavelet Decomposition ◽  
State Of The Art ◽  
Error Rates ◽  
Signature Verification ◽  
Signature Analysis ◽  
Discriminative Power ◽  
Feature Sets ◽  
Online Signature ◽  
Online Signature Verification

In this paper, the discriminative power of a set of features which seems to be relevant to signature analysis by Forensic Handwriting Experts (FHEs) is analyzed and particularly compared to the discriminative power of automatically selected feature sets. This analysis could help FHEs to further understand the signatures and the writer behaviour. In addition, two information fusion schemes are proposed to combine the discriminative capability of the two types of features being considered. The coefficients in the wavelet decomposition of the different time functions associated with the signing process are used as features to model them. Two different signature styles are considered, namely, Western and Chinese, of one of the most recent publicly available Online Signature Databases. The experimental results are promising, especially for the features that seem to be relevant to FHEs, since the obtained verification error rates are comparable to the ones reported in the state-of-the-art over the same datasets. Further, the results also show that it is possible to combine both types of features to improve the verification performance. Purchase Article for $10 

Chaotic Liquid State Machine

2015 ◽  
Vol 9 (4) ◽  
pp. 1-20
Author(s):  
Mario Antoine Aoun ◽  
Mounir Boukadoum
Keyword(s):  
Liquid State ◽  
State Machine ◽  
Spiking Neurons ◽  
Signature Verification ◽  
Time Varying ◽  
Plasticity Mechanism ◽  
Online Signature ◽  
Online Signature Verification ◽  
One Class Classifier ◽  
Classification Capability

The authors implement a Liquid State Machine composed from a pool of chaotic spiking neurons. Furthermore, a synaptic plasticity mechanism operates on the connection weights between the neurons inside the pool. A special feature of the system's classification capability is that it can learn the class of a set of time varying inputs when trained from positive examples only, thus, it is a one class classifier. To demonstrate the applicability of this novel neurocomputing architecture, the authors apply it for Online Signature Verification.

scholarly journals Online signature verification using neural network and pearson correlation features

2013 ◽  
Author(s):  
Vahab Iranmanesh ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Wan Azizun Wan Adnan ◽  
Fahad Layth Malallah ◽  
Salman Yussof
Keyword(s):  
Neural Network ◽  
Pearson Correlation ◽  
Signature Verification ◽  
Online Signature ◽  
Online Signature Verification
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