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Found 4 papers, 0 papers with code
Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification
This paper introduces a novel approach to leverage the knowledge of existing expert models for training new Convolutional Neural Networks, on domains where task-specific data are limited or unavailable.
Sequential Motif Profiles and Topological Plots for Offline Signature Verification
The local visibility graph features are considered to be highly informative for SV; this is sustained by the corresponding results which are at least comparable with other classic state-of-the-art approaches.
A comprehensive study of sparse representation techniques for offline signature verification
In this work, a feature extraction method for offline signature verification is presented that harnesses the power of sparse representation in order to deliver state-of-the-art verification performance in several signature datasets like CEDAR, MCYT-75, GPDS and UTSIG.
Offline Handwritten Signature Modeling and Verification Based on Archetypal Analysis
The output of the archetypal analysis of few reference samples is a set of archetypes which are used to form the base of the feature space.
