A User-Centered Framework for Adaptive Fingerprint Identification

Title
A User-Centered Framework for Adaptive Fingerprint Identification
Publication Date
2009
Author(s)
Kwan, Paul W H
Gao, Junbin
Leedham, Graham
Editor
Editor(s): Chen Hsinchun, Christopher C Yang, Michael Chau and L Shu-Hsing
Type of document
Conference Publication
Language
en
Entity Type
Publication
Publisher
Springer
Place of publication
Berlin, Germany
Edition
1
Series
Lecture Notes in Computer Science
DOI
10.1007/978-3-642-01393-5_10
UNE publication id
une:5716
Abstract
In recent years, law enforcement personnel have been greatly aided by the deployment of automated fingerprint identification systems (AFIS). These "black-box" systems largely operate by matching distinctive features automatically extracted from fingerprint images for their decisions. However, current systems have two major shortcomings. First, the identification result depends solely on the chosen features and the algorithm that matches them. Second, these systems cannot improve their results by benefiting from interactions with expert examiners who often can identify small differences between fingerprints. In this paper, we demonstrate by incorporating Relevance Feedback in a fingerprint identification system as an add-on module, a persistent semantic space over the database of fingerprints for an expert user can be incrementally learned. Here, the learning module makes use of a Dimensionality Reduction process that returns both a low-dimensional semantic space and an out-of-sample mapping function, achieving a two-fold benefits of data compression and the ability to project novel fingerprints directly onto the semantic space for identification. Experimental results demonstrated the potential of this user-centered framework for adaptive fingerprint identification.
Link
Citation
Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2009, Bangkok, Thailand, April 27, 2009. Proceedings, p. 89-100
ISBN
9783642013935
9783642013928
3642013937
3642013929
Start page
89
End page
100

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