Analyzing features learned for Offline Signature Verification using Deep CNNs

Abstract

Research on Offline Handwritten Signature Verification explored a large variety of handcrafted feature extractors, ranging from graphology, texture descriptors to interest points. In spite of advancements in the last decades, performance of such systems is still far from optimal when we test the systems against skilled forgeries - signature forgeries that… (More)
DOI: 10.1109/ICPR.2016.7900092

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Cite this paper

@article{Hafemann2016AnalyzingFL, title={Analyzing features learned for Offline Signature Verification using Deep CNNs}, author={Luiz G. Hafemann and Robert Sabourin and Luiz Eduardo Soares de Oliveira}, journal={2016 23rd International Conference on Pattern Recognition (ICPR)}, year={2016}, pages={2989-2994} }