Abstract
Handwritten signature verification is a challenging problem due to the high similarity between genuine signatures and skilled forgeries. In this paper, we propose a novel framework for off-line signature verification using a Deep Convolutional Siamese Network for metric learning. For improving the discrimination ability, we extract features from local regions instead of the whole signature image and fuse the similarity measures of multiple regions for verification. Feature extractors of different regions share the convolutional layers in the convolutional network, which is trained with signature image pairs. In experiments on the benchmark datasets CEDAR and GPDS, the proposed method achieved 4.55% EER and 8.89% EER, respectively, which are competitive to state-of-the-art approaches.
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