The application of the Bayes Ying Yang harmony based GMMs in on-line signature verification

TL;DR

This paper introduces a novel on-line signature verification method using Bayes Ying Yang harmony-based Gaussian Mixture Models, which automatically select optimal models during training, leading to improved verification accuracy.

Contribution

The paper presents a new approach combining BYY harmony with GMMs for automatic model selection in signature verification, enhancing model accuracy.

Findings

Achieved satisfactory verification results on SVC 2004 database.

Demonstrated the effectiveness of BYY harmony in model selection.

Improved signature verification accuracy over traditional methods.

Abstract

In this contribution, a Bayes Ying Yang(BYY) harmony based approach for on-line signature verification is presented. In the proposed method, a simple but effective Gaussian Mixture Models(GMMs) is used to represent for each user's signature model based on the prior information collected. Different from the early works, in this paper, we use the Bayes Ying Yang machine combined with the harmony function to achieve Automatic Model Selection(AMS) during the parameter learning for the GMMs, so that a better approximation of the user model is assured. Experiments on a database from the First International Signature Verification Competition(SVC 2004) confirm that this combined algorithm yields quite satisfactory results.