Abstract
Detection of Fake Signatures is a Hard Task. in This Paper, We Present a Novel Method for Detecting Trained Forgeries using Features Extracted from Sliding Windows with Different overlaps on a Publicly Available Dataset of Static Images of Signatures. using a Linear Machine Learning Model Named Extreme Learning Machine (Elm), Our Methodology Achieves, in Average, an Equal Error Rates (Eer) of 2.31% for an overlap of 90%. in Line with the State-Of-The-Art Results Available in the Scientific Literature.
Recommended Citation
A. Akusok et al., "Handwriting Features based Detection of Fake Signatures," ACM International Conference Proceeding Series, pp. 86 - 89, Association for Computing Machinery, Jun 2021.
The definitive version is available at https://doi.org/10.1145/3453892.3454003
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
biometrics; neural networks; Signature verification
International Standard Book Number (ISBN)
978-145038792-7
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
29 Jun 2021
