K-nearest neighbour and dynamic time warping for online signature verification

TL;DR

This paper introduces a signature verification system combining k-nearest neighbor and dynamic time warping, tested on DeepSignDB, showing promising accuracy for both finger and stylus inputs in various scenarios.

Contribution

It presents a novel verification approach using KNN and DTW on a new large-scale database, applicable to both mobile and office signature inputs.

Findings

Error rate of 6.04% for stylus signatures

Error rate of 5.20% for finger signatures

Effective on both development and evaluation sets

Abstract

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 k-nearest 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.