A Secure and Light Weight User Authentication System Based on Online Signature Verification for Resource Constrained Mobile Networks

2019 ◽  
pp. 133-140
Author(s):  
Chandra Sekhar Vorugunti ◽  
D. S. Guru ◽  
Viswanath Pulabaigari
Keyword(s):  
Mobile Networks ◽  
User Authentication ◽  
Signature Verification ◽  
Light Weight ◽  
Resource Constrained ◽  
Online Signature ◽  
Online Signature Verification ◽  
Authentication System

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.

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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