  Fully convolutional networks (FCNs) have been recently used for feature
extraction and classification in image and speech recognition, where their
inputs have been raw signal or other complicated features. Persian signature
verification is done using conventional convolutional neural networks (CNNs).
In this paper, we propose to use FCN for learning a robust feature extraction
from the raw signature images. FCN can be considered as a variant of CNN where
its fully connected layers are replaced with a global pooling layer. In the
proposed manner, FCN inputs are raw signature images and convolution filter
size is fixed. Recognition accuracy on UTSig database, shows that FCN with a
global average pooling outperforms CNN.
