An Efficient Transfer Learning Model for Predicting Forged (Handwritten) Signature-Reference-Cited by-同舟云学术

An Efficient Transfer Learning Model for Predicting Forged (Handwritten) Signature

Published:2021-12-26 Issue: Volume: Page:
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Container-title:2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)
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Author:

Sheikh Muhammad Rafsun¹,Masud Tarek Hasan¹,Khan Nafiz Imtiaz¹,Islam Muhammad Nazrul¹

Affiliation:

1. Military Institute of Science and Technology,Department of Computer Science and Engineering,Dhaka,Bangladesh,1216

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9768399/9768406/09768440.pdf?arnumber=9768440

Reference20 articles.

1. Offline geometric parameters for automatic signature verification using fixed-point arithmetic

2. Off-Line Signature Verification Based on Feature Matching

3. A new scheme for offline signature verification using dwt and fuzzy net;tian;Eighth ACIS International Conference on Software Engineering Artificial Intelligence Networking and Parallel/Distributed Computing (SNPD 2007),0

4. Learning features for offline handwritten signature verification using deep convolutional neural networks

5. Off-line signature verification by the tracking of feature and stroke positions

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