Fusion of Multiple Matchers using SVM for Offline Signature Identification

TL;DR

This paper presents a method that combines multiple classifiers using SVM to improve offline signature identification accuracy, leveraging global and local features and various distance-based classifiers.

Contribution

It introduces a novel fusion approach of multiple matchers using SVM for offline signature verification, enhancing recognition performance.

Findings

Achieved promising recognition accuracy on a large signature database.

Effective fusion of classifiers improves verification performance.

Demonstrated robustness with diverse feature extraction methods.

Abstract

This paper uses Support Vector Machines (SVM) to fuse multiple classifiers for an offline signature system. From the signature images, global and local features are extracted and the signatures are verified with the help of Gaussian empirical rule, Euclidean and Mahalanobis distance based classifiers. SVM is used to fuse matching scores of these matchers. Finally, recognition of query signatures is done by comparing it with all signatures of the database. The proposed system is tested on a signature database contains 5400 offline signatures of 600 individuals and the results are found to be promising.