Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/5584
Title: A User-Centered Framework for Adaptive Fingerprint Identification
Contributor(s): Kwan, Paul W H (author); Gao, Junbin (author); Leedham, Graham (author)
Publication Date: 2009
DOI: 10.1007/978-3-642-01393-5_10
Handle Link: https://hdl.handle.net/1959.11/5584
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.
Publication Type: Conference Publication
Source of Publication: Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2009 Bangkok, Thailand, April 27, 2009 Proceedings, p. 89-100
Publisher: Springer-Verlag
Place of Publication: Berlin/Heidelburg, Germany
ISBN: 9783642013935
9783642013928
3642013937
3642013929
Field of Research (FOR): 080108 Neural, Evolutionary and Fuzzy Computation
080106 Image Processing
080109 Pattern Recognition and Data Mining
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Other Links: http://trove.nla.gov.au/work/36729392
Series Name: Lecture Notes in Computer Science
Series Number : 5477
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Appears in Collections:Book Chapter
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