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AuthorRida I.
AuthorMaadeed S.A.
AuthorJiang X.
AuthorLunke F.
AuthorBensrhair A.
Available date2020-03-03T06:19:33Z
Publication Date2018
Publication NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ResourceScopus
ISSN15206149
URIhttp://dx.doi.org/10.1109/ICASSP.2018.8462051
URIhttp://hdl.handle.net/10576/13163
AbstractPalmprint recognition is an important and widely used biometric modality with high reliability, stability and user acceptability. In this paper we propose a simple and effective ensemble learning method for palmprint identification based on Random Subspace Sampling (RSS). To achieve it, we rely on 2D-PCA to build the random subspaces. As 2D-PCA is an unsurpevised technique, features are extracted in each subspace using 2D-LDA. A simple 1-Nearest Neighbor classifier is associated to each subspace, the final decision rule being obtained by majority voting rule. The experimental results on multispectral and PolyU palmprint datasets show very encouraging performances compared to state-of-the-art techniques.
SponsorThis publication was made possible using a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 7-1711-1-312. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBiometrics
Ensemble learning
Palmprint
Random subspace sampling
TitleAn Ensemble Learning Method Based on Random Subspace Sampling for Palmprint Identification
TypeConference Paper
Pagination2047 - 2051
Volume Number2018-April
dc.accessType Abstract Only


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