  Offline signature verification is one of the most challenging tasks in
biometrics and document forensics. Unlike other verification problems, it needs
to model minute but critical details between genuine and forged signatures,
because a skilled falsification might often resembles the real signature with
small deformation. This verification task is even harder in writer independent
scenarios which is undeniably fiscal for realistic cases. In this paper, we
model an offline writer independent signature verification task with a
convolutional Siamese network. Siamese networks are twin networks with shared
weights, which can be trained to learn a feature space where similar
observations are placed in proximity. This is achieved by exposing the network
to a pair of similar and dissimilar observations and minimizing the Euclidean
distance between similar pairs while simultaneously maximizing it between
dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the
capability of our network to model forgery in different languages (scripts) and
handwriting styles. Moreover, our designed Siamese network, named SigNet,
exceeds the state-of-the-art results on most of the benchmark signature
datasets, which paves the way for further research in this direction.
