A knowledge-based Decision Support System for adaptive fingerprint identification that uses relevance feedback

Title
A knowledge-based Decision Support System for adaptive fingerprint identification that uses relevance feedback
Publication Date
2015
Author(s)
Kwan, Paul H
Welch, Mitchell
( author )
OrcID: https://orcid.org/0000-0003-4220-8734
Email: mwelch8@une.edu.au
UNE Id une-id:mwelch8
Foley, Jacob
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
Elsevier BV
Place of publication
Netherlands
DOI
10.1016/j.knosys.2014.10.005
UNE publication id
une:16629
Abstract
In this paper, the use of 'relevance feedback' with fingerprint identification systems is investigated. Two key limitations in current systems are addressed. Firstly, performance in current systems is highly dependent upon the fingerprint features selected for identification and the accuracy of the underlying pattern matching algorithm. Secondly, there is no effective mechanism to improve future queries through knowledge captured from the users, who are often experienced fingerprint examiners. Relevance feedback, a human computer interaction technique to capture and re-use knowledge of a user, has been studied extensively in text-based document retrieval systems and content-based image retrieval systems, but to date examples of its application to fingerprint identification systems are rare. By exploiting relevance feedback, this paper presents a user-centric and adaptive framework that allows tacit knowledge of fingerprint examiners to be captured and re-used to enhance their future decisions. The outcome is a knowledge- based Decision Support System (DSS) that provides the examiner with both intuitive visualization displays to analyze the relationships between images in the fingerprint database and relevance feedback facility to produce a persistent and personalized semantic space overlay. This serves a long term memory that can be updated to reflect the knowledge captured from the user. Empirical experiments confirmed the ability of this approach to improve the accuracy of fingerprint identification queries when compared to the static data processing architecture of current systems.
Link
Citation
Knowledge-Based Systems, v.73, p. 236-253
ISSN
1872-7409
0950-7051
Start page
236
End page
253

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