http://iet.metastore.ingenta.com
1887

Signature authentication based on human intervention: performance and complementarity with automatic systems

Signature authentication based on human intervention: performance and complementarity with automatic systems

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
— Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This work explores human intervention to improve Automatic Signature Verification (ASV). Significant efforts have been made in order to improve the performance of ASV algorithms over the last decades. This work analyzes how human actions can be used to complement automatic systems. Which actions to take and to what extent those actions can help state-of-the-art ASV systems is the final aim of this research line. The analysis at classification level comprises experiments with responses from 500 people based on crowdsourcing signature authentication tasks. The results allow to establish a human baseline performance and comparison with automatic systems. Intervention at feature extraction level is evaluated using a self-developed tool for the manual annotation of signature attributes inspired in Forensic Document Experts analysis. We analyze the performance of attribute-based human signature authentication and its complementarity with automatic systems. The experiments are carried out over a public database including the two most popular signature authentication scenarios based on both online (dynamic time sequences including position and pressure) and offline (static images) information. The results demonstrate the potential of human interventions at feature extraction level (by manually annotating signature attributes) and encourage to further research in its capabilities to improve the performance of ASV.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2016.0115
Loading

Related content

content/journals/10.1049/iet-bmt.2016.0115
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address