Methods for learning feature representations for offline handwritten signature verification
have been successfully proposed in recent literature, using deep convolutional neural …

This article presents SVC-onGoing 1, an on-going competition for on-line signature
verification where researchers can easily benchmark their systems against the state of the …

The area of Handwritten Signature Verification has been broadly researched in the last
decades, but remains an open research problem. The objective of signature verification …

Offline handwritten signature verification is one of the most prevalent and prominent
biometric methods in many application fields. Siamese neural network, which can extract …

Handwritten signature verification is a challenging task because signatures of a writer may
be skillfully imitated by a forger. As skilled forgeries are generally difficult to acquire for …

In recent years, with the rapid advancements in deep learning technologies, particularly
deep neural networks, signature verification has seen significant improvements in accuracy …

Offline signature verification serves as a robust authentication method, widely adopted in
security applications due to its non-intrusive data collection procedure. Despite the notable …

In recent years, the performance of handwritten signature verification (HSV) has been
considerably improved by deep learning methods. However, deep HSV still faces significant …

In today's world it is necessary to protect one's authenticity in order to ensure the protection
of personal information that only the authenticate credentials of a person can have access …

In writer-independent verification systems, a single model is trained for all users of the
system using dissimilarity vectors obtained through a dichotomy transformation that converts …