  Methods for learning feature representations for Offline Handwritten
Signature Verification have been successfully proposed in recent literature,
using Deep Convolutional Neural Networks to learn representations from
signature pixels. Such methods reported large performance improvements compared
to handcrafted feature extractors. However, they also introduced an important
constraint: the inputs to the neural networks must have a fixed size, while
signatures vary significantly in size between different users. In this paper we
propose addressing this issue by learning a fixed-sized representation from
variable-sized signatures by modifying the network architecture, using Spatial
Pyramid Pooling. We also investigate the impact of the resolution of the images
used for training, and the impact of adapting (fine-tuning) the representations
to new operating conditions (different acquisition protocols, such as writing
instruments and scan resolution). On the GPDS dataset, we achieve results
comparable with the state-of-the-art, while removing the constraint of having a
maximum size for the signatures to be processed. We also show that using higher
resolutions (300 or 600dpi) can improve performance when skilled forgeries from
a subset of users are available for feature learning, but lower resolutions
(around 100dpi) can be used if only genuine signatures are used. Lastly, we
show that fine-tuning can improve performance when the operating conditions
change.
