A minimum-velocity-based segmentation scheme for improved performance of an online signature verification system

2008 ◽  
Author(s):  
Tarig A. Osman ◽  
Mohan Krishnan ◽  
Mark J. Paulik
Keyword(s):  
Signature Verification ◽  
Verification System ◽  
Online Signature ◽  
Online Signature Verification ◽  
Improved Performance ◽  
Minimum Velocity

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 Optimized jk-nearest neighbor based online signature verification and evaluation of the main parameters

2021 ◽  
Vol 22 (4) ◽  
Author(s):  
Mohammad Saleem ◽  
Bence Kovari
Keyword(s):  
Error Rate ◽  
Nearest Neighbor ◽  
Real Life ◽  
Signature Verification ◽  
Average Error ◽  
Reasonable Accuracy ◽  
Verification System ◽  
Optimal Value ◽  
Online Signature ◽  
Online Signature Verification

In this paper, we propose an enhanced jk-nearest neighbor (jk-NN) classifier for online signature verification. After studying the algorithm's main parameters, we use four separate databases to present and evaluate each algorithm parameter. The results show that the proposed method can increase the verification accuracy by 0.73-10% compared to a traditional one class k-NN classifier. The algorithm has achieved reasonable accuracy for different databases, a 3.93% error rate when using the SVC2004 database, 2.6% for MCYT-100 database, 1.75% for the SigComp'11 database, and 6% for the SigComp'15 database.The proposed algorithm uses specifically chosen parameters and a procedure to pick the optimal value for K using only the signer's reference signatures, to build a practical verification system for real-life scenarios where only these signatures are available. By applying the proposed algorithm, the average error achieved was 8% for SVC2004, 3.26% for MCYT-100, 13% for SigComp'15, and 2.22% for SigComp'11.

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