Offline Signature Verification Based on Feature Disentangling Aided Variational Autoencoder

TL;DR

This paper introduces a novel offline signature verification method using a variational autoencoder with feature disentangling, improving discrimination between genuine signatures and forgeries, especially when skilled forgeries are scarce.

Contribution

It is the first to employ a VAE with a new loss function for feature disentangling in signature verification, enhancing feature discrimination and robustness.

Findings

Significantly outperformed 13 existing methods on public datasets.

Demonstrated robustness and potential for real-world application.

Achieved notable improvements in accuracy on MCYT-75 and GPDS-synthetic datasets.

Abstract

Offline handwritten signature verification systems are used to verify the identity of individuals, through recognizing their handwritten signature image as genuine signatures or forgeries. The main tasks of signature verification systems include extracting features from signature images and training a classifier for classification. The challenges of these tasks are twofold. First, genuine signatures and skilled forgeries are highly similar in their appearances, resulting in a small inter-class distance. Second, the instances of skilled forgeries are often unavailable, when signature verification models are being trained. To tackle these problems, this paper proposes a new signature verification method. It is the first model that employs a variational autoencoder (VAE) to extract features directly from signature images. To make the features more discriminative, it improves the traditional VAEs by introducing a new loss function for feature disentangling. In addition, it relies on SVM (Support Vector Machine) for classification according to the extracted features. Extensive experiments are conducted on two public datasets: MCYT-75 and GPDS-synthetic where the proposed method significantly outperformed $13$ representative offline signature verification methods. The achieved improvement in distinctive datasets indicates the robustness and great potential of the developed system in real application.