Consensus-Threshold Criterion for Offline Signature Verification using Convolutional Neural Network Learned Representations
This work addresses the risk of imposters accessing sensitive documents or transactions in signature verification systems, representing an incremental improvement over existing methods.
The paper tackles the problem of high false acceptance rates in offline signature verification by proposing a consensus-threshold distance-based classifier using features from SigNet and SigNet-F models, achieving a 1.27% FAR on the GPDS-300 dataset compared to previous rates of 8.73% and 17.31%.
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.