Consensus-Threshold Criterion for Offline Signature Verification using
  Convolutional Neural Network Learned Representations
Paul Brimoh Chollette C. Olisah
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.