Signature Verification Approach using Fusion of Hybrid Texture Features

TL;DR

This paper presents a writer-dependent signature verification method that combines Wavelet and Local Quantized Pattern features with score fusion of one-class SVMs, achieving superior accuracy across multiple datasets.

Contribution

It introduces a novel fusion approach using hybrid texture features and one-class SVMs trained only on genuine signatures for improved verification.

Findings

Outperforms existing signature verification systems

Effective across four public datasets

Utilizes only genuine signatures for training

Abstract

In this paper, a writer-dependent signature verification method is proposed. Two different types of texture features, namely Wavelet and Local Quantized Patterns (LQP) features, are employed to extract two kinds of transform and statistical based information from signature images. For each writer two separate one-class support vector machines (SVMs) corresponding to each set of LQP and Wavelet features are trained to obtain two different authenticity scores for a given signature. Finally, a score level classifier fusion method is used to integrate the scores obtained from the two one-class SVMs to achieve the verification score. In the proposed method only genuine signatures are used to train the one-class SVMs. The proposed signature verification method has been tested using four different publicly available datasets and the results demonstrate the generality of the proposed method. The proposed system outperforms other existing systems in the literature.