  Signature verification, as a crucial practical documentation analysis task,
has been continuously studied by researchers in machine learning and pattern
recognition fields. In specific scenarios like confirming financial documents
and legal instruments, ensuring the absolute reliability of signatures is of
top priority. In this work, we proposed a new method to learn "top-rank pairs"
for writer-independent offline signature verification tasks. By this scheme, it
is possible to maximize the number of absolutely reliable signatures. More
precisely, our method to learn top-rank pairs aims at pushing positive samples
beyond negative samples, after pairing each of them with a genuine reference
signature. In the experiment, BHSig-B and BHSig-H datasets are used for
evaluation, on which the proposed model achieves overwhelming better pos@top
(the ratio of absolute top positive samples to all of the positive samples)
while showing encouraging performance on both Area Under the Curve (AUC) and
accuracy.
