An Algorithm for Automatically Choosing Distractors for Recognition Based Authentication using Minimal Image Types
Ron Poet*, Karen Renaud*
Identifiers and Pagination:Year: 2009
First Page: 178
Last Page: 184
Publisher Id: TOERGJ-2-178
Article History:Received Date: 27/05/2009
Revision Received Date: 09/09/2009
Acceptance Date: 10/09/2009
Electronic publication date: 24/12/2009
Collection year: 2009
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
When a user logs on to a recognition based authentication system, he or she is presented with a number of images, one of which is their pass image and the others are distractors. The user must recognise and select their own image to enter the system. If any of the distractors is too similar to the target, the user is likely to become confused and may well choose a distractor by mistake.
It is simple for humans to rule on image similarity but such a labour intensive approach hinders the wider uptake of these mechanisms. Automating image similarity detection is a challenging problem but somewhat easier when the images being used are minimal image types such as hand drawn doodles and Mikons constructed using a computer tool.
We have developed an algorithm, which has been reported earlier, to automatically detect if two doodle images are similar. This paper reports a new experiment to discover the amount of similarity in collections of doodles and Mikons, from a human perspective. This information is used to improve the algorithm and confirm that it also works well with Mikons.