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GridLDA of Gabor wavelet features for palmprint identification

Published: 23 August 2012 Publication History

Abstract

In this paper, we propose a novel palmprint recognition algorithm based on using GridLDA for Gabor wavelet features. Our proposed method includes two main steps for palmprint feature extraction: (1) Local invariant features are extracted by computing the Gabor wavelet Engergy of the original images that handles the palm structure and the variations of illumination. (2) An improved two-dimensional Linear Discriminant Analysis, called GridLDA, is then applied to further remove redundant information and form a discriminant representation more suitable for palmprint recognition. The experimental results for the identification on public database of Hong Kong Polytechnic University (PolyU) demonstrate the effectiveness of the proposed method.

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Cited By

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  • (2019)Palmprint Recognition Using Discriminant Local Line Directional RepresentationAdvanced Computational Methods for Knowledge Engineering10.1007/978-3-030-38364-0_19(208-217)Online publication date: 20-Dec-2019
  • (2017)Equipment health modeling for deterioration prognosis and fault signatures diagnosis2017 28th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)10.1109/ASMC.2017.7969260(357-362)Online publication date: May-2017
  • (2015)Efficient palmprint identification using novel symmetry filter and alignment refinementInternational Journal of Biometrics10.1504/IJBM.2015.0719437:3(213-225)Online publication date: 1-Sep-2015
  • Show More Cited By

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Published In

cover image ACM Other conferences
SoICT '12: Proceedings of the 3rd Symposium on Information and Communication Technology
August 2012
290 pages
ISBN:9781450312325
DOI:10.1145/2350716
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2012

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Author Tags

  1. 2DLDA
  2. Gabor wavelets
  3. GridLDA
  4. identification
  5. palmprint identification
  6. palmprint recognition

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SoICT '12

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Overall Acceptance Rate 147 of 318 submissions, 46%

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Cited By

View all
  • (2019)Palmprint Recognition Using Discriminant Local Line Directional RepresentationAdvanced Computational Methods for Knowledge Engineering10.1007/978-3-030-38364-0_19(208-217)Online publication date: 20-Dec-2019
  • (2017)Equipment health modeling for deterioration prognosis and fault signatures diagnosis2017 28th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)10.1109/ASMC.2017.7969260(357-362)Online publication date: May-2017
  • (2015)Efficient palmprint identification using novel symmetry filter and alignment refinementInternational Journal of Biometrics10.1504/IJBM.2015.0719437:3(213-225)Online publication date: 1-Sep-2015
  • (2015)Efficient Palmprint Search Based on Database Clustering for Personal IdentificationKnowledge and Systems Engineering10.1007/978-3-319-11680-8_31(393-404)Online publication date: 2015
  • (2014)On Discriminant Orientation Extraction Using GridLDA of Line Orientation Maps for Palmprint IdentificationKnowledge and Systems Engineering10.1007/978-3-319-02741-8_21(237-248)Online publication date: 2014

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