# Consensus-Threshold Criterion for Offline Signature Verification using Convolutional Neural Network Learned Representations

arXiv: https://arxiv.org/abs/2401.03085 · HTML: https://mlsanity.com/paper/2401.03085/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 5 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1705.05787/) (2017)
- [Offline Handwritten Signature Verification - Literature Review](https://mlsanity.com/paper/1507.07909/) (2015)
- [Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1604.00974/) (2016)
- [A writer-independent approach for offline signature verification using deep convolutional neural networks features](https://mlsanity.com/paper/1807.10755/) (2018)
- [Multi-Representational Learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs](https://mlsanity.com/paper/1903.06536/) (2019)

## Abstract (arXiv)

A genuine signer's signature is naturally unstable even at short time-intervals whereas, expert forgers always try to perfectly mimic a genuine signer's signature. This presents a challenge which puts a genuine signer at risk of being denied access, while a forge signer is granted access. The implication is a high false acceptance rate (FAR) which is the percentage of forge signature classified as belonging to a genuine class. Existing work have only scratched the surface of signature verification because the misclassification error remains high. In this paper, a consensus-threshold distance-based classifier criterion is proposed for offline writer-dependent signature verification. Using features extracted from SigNet and SigNet-F deep convolutional neural network models, the proposed classifier minimizes FAR. This is demonstrated via experiments on four datasets: GPDS-300, MCYT, CEDAR and Brazilian PUC-PR datasets. On GPDS-300, the consensus threshold classifier improves the state-of-the-art performance by achieving a 1.27% FAR compared to 8.73% and 17.31% recorded in literature. This performance is consistent across other datasets and guarantees that the risk of imposters gaining access to sensitive documents or transactions is minimal.