A Novel Multiple Distances Based Dynamic Time Warping Method for Online Signature Verification

2018 ◽  
pp. 645-652
Author(s):  
Xinyi Lu ◽  
Yuxun Fang ◽  
Qiuxia Wu ◽  
Junhong Zhao ◽  
Wenxiong Kang
Keyword(s):  
Dynamic Time Warping ◽  
Signature Verification ◽  
Time Warping ◽  
Online Signature ◽  
Online Signature Verification ◽  
Dynamic Time

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 Preprocessing approaches in DTW based online signature verification

2020 ◽  
Vol 15 (1) ◽  
pp. 148-157
Author(s):  
Mohammad Saleem ◽  
Bence Kovari
Keyword(s):  
Dynamic Time Warping ◽  
Signature Verification ◽  
Vertical Scaling ◽  
Time Warping ◽  
Online Signature ◽  
Different Dimensions ◽  
Online Signature Verification ◽  
Dynamic Time

Abstract Amongst different approaches, dynamic time warping has shown promising results during the online signature verification competitions of previous years. To improve the results of dynamic time warping, different preprocessing steps may be applied and different dimensions of the samples may be compared. The choice of preprocessing steps and comparing dimensions may significantly influence the results. Thus, to aid researchers with these decisions, a comparison made between the results of promising preprocessing algorithms as horizontal scaling, vertical scaling and alignment using dynamic time warping in different dimensions and their combinations on two datasets (SVC2004 and MCYT-100). The results showed that preprocessing methods made a very promising improvement in the verification accuracy.

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.

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