scholarly journals Online signature verification using hybrid wavelet transform

2020 ◽  
Vol 10 (2) ◽  
pp. 1823
Author(s):  
Manoj Chavan ◽  
Ravish R. Singh ◽  
Vinayak Bharadi
Keyword(s):  
Wavelet Transform ◽  
Kronecker Product ◽  
Feature Vector ◽  
Local Level ◽  
Signature Verification ◽  
Dynamic Features ◽  
Online Signature ◽  
Pressure Parameter ◽  
Online Signature Verification ◽  
Biometric Trait

Online signature verification is a prominent behavioral biometric trait. It offers many dynamic features along with static two dimensional signature image. In this paper, the Hybrid Wavelet Transform (HWT) was generated using Kronecker product of two orthogonal transform such as DCT, DHT, Haar, Hadamard and Kekre. HWT has the ability to analyze the signal at global as well as local level like wavelet transform. HWT-1 and -2 was applied on the first 128 samples of the pressure parameter and first 16 samples of the output were used as feature vector for signature verification. This feature vector is given to Left to Right HMM classifier to identify the genuine and forged signature. For HWT-1, DCT HAAR offers best FAR and FRR. . For HWT-2, KEKRE 128 offers best FAR and FRR. HWT-1 offers better performance than HWT- 2 in terms of FAR and FRR. As the number of states increase, the performance of the system improves. For HWT - 1, KEKRE 128 offers best performance at 275 symbols whereas for HWT - 2, best performance is at 475 symbols by KEKRE 128.

scholarly journals A Two-Stage Method for Online Signature Verification Using Shape Contexts and Function Features

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1808 ◽  
Author(s):  
Yu Jia ◽  
Linlin Huang ◽  
Houjin Chen
Keyword(s):  
Signature Verification ◽  
Time Warping ◽  
Shape Context ◽  
Two Stage ◽  
Online Signature ◽  
Online Signature Verification ◽  
Dynamic Time ◽  
And Function ◽  
Interval Valued ◽  
Biometric Trait

As a behavioral biometric trait, an online signature is extensively used to verify a person’s identity in many applications. In this paper, we present a method using shape contexts and function features as well as a two-stage strategy for accurate online signature verification. Specifically, in the first stage, features of shape contexts are extracted from the input and classification is made based on distance metric. Only the inputs passing by the first stage are represented by a set of function features and verified. To improve the matching accuracy and efficiency, we propose shape context-dynamic time warping (SC-DTW) to compare the test signature with the enrolled reference ones based on the extracted function features. Then, classification based on interval-valued symbolic representation is employed to decide if the test signature is a genuine one. The proposed method is evaluated on SVC2004 Task 2 achieving an Equal Error Rate of 2.39% which is competitive to the state-of-the-art approaches. The experiment results demonstrate the effectiveness of the proposed method.

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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