Luiz G. Hafemann

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The area of Handwritten Signature Verification has been broadly researched in the last decades and still remains as an open research problem. This report focuses on offline signature verification, characterized by the usage of static (scanned) images of signatures, where the objective is to discriminate if a given signature is genuine (produced by the(More)
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)
Automatic Offline Handwritten Signature Verification has been researched over the last few decades from several perspectives, using insights from graphology, computer vision, signal processing, among others. In spite of the advancements on the field, building classifiers that can separate between genuine signatures and skilled forgeries (forgeries made(More)
Convolutional Neural Networks (CNNs) have set the state-of-the-art in many computer vision tasks in recent years. For this type of model, it is common to have millions of parameters to train, commonly requiring large datasets. We investigate a method to transfer learning across different texture classification problems, using CNNs, in order to take(More)
Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a person’s signature and deliberately attempt to imitate it. In offline (static) signature verification, the dynamic information of the signature writing process is lost, and it is difficult to design good feature(More)
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