A Learning Framework for Examiner-Centric Fingerprint Classification using Spectral Features

Paul Kwan, Yi Guo, Junbin Gao

Research output: Book chapter/Published conference paperConference paper

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Abstract

In recent years, the tasks of fingerprint examiners have been greatly aided by the development of automatic fingerprint classification systems. These systems operate by matching low-level features automatically extracted from fingerprint images, often represented collectively as numeric vectors, for their decision. However, there are two major shortcomings in current systems. First, the result of classification depends solely on the chosen features and the algorithm that matches them. Second, the systems cannot adapt their results over time through interaction with individual fingerprint examiners who often have different degrees of experiences. In this paper, we demonstrate by incorporating relevance feedback in a fingerprint classification system, a personalized semantic space over the database of fingerprints for each user can be incrementally learned. The fingerprint features that induce the initial features space from which individual semantic spaces are being learned were obtained by multispectral decomposition of fingerprints using a bank of Gabor filters. In this learning framework, the out-of-sample extension of a recently introduced dimensionality reduction method, called Twin Kernel Embedding (TKE), is applied to learn both the semantic space and a mapping function for classifying novel fingerprints. Experimental results confirm this learning framework for examiner-centric fingerprint classification.
Original languageEnglish
Title of host publicationMIPPR 2007
Subtitle of host publicationAutomatic target recognition and image analysis; and multispectral image acquisition
EditorsTianxu Zhang, Carl A Nardell, Duane D Smith, Hangqing Lu
Place of PublicationWashington, USA
PublisherSPIE
Pages67881H
Volume6788
DOIs
Publication statusPublished - 2007
EventInternational Symposium on Multispectrum Image Processing and Pattern Recognition (MIPPR) - Wuhan, China, China
Duration: 15 Nov 200717 Nov 2007

Conference

ConferenceInternational Symposium on Multispectrum Image Processing and Pattern Recognition (MIPPR)
CountryChina
Period15/11/0717/11/07

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    Kwan, P., Guo, Y., & Gao, J. (2007). A Learning Framework for Examiner-Centric Fingerprint Classification using Spectral Features. In T. Zhang, C. A. Nardell, D. D. Smith, & H. Lu (Eds.), MIPPR 2007: Automatic target recognition and image analysis; and multispectral image acquisition (Vol. 6788, pp. 67881H). SPIE. https://doi.org/10.1117/12.749777