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Optical Engineering

Palm vein recognition based on directional empirical mode decomposition
Author(s): Jen-Chun Lee; Chien-Ping Chang; Wei-Kuei Chen
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Paper Abstract

Directional empirical mode decomposition (DEMD) has recently been proposed to make empirical mode decomposition suitable for the processing of texture analysis. Using DEMD, samples are decomposed into a series of images, referred to as two-dimensional intrinsic mode functions (2-D IMFs), from finer to large scale. A DEMD-based (2-D) 2 linear discriminant analysis (LDA) for palm vein recognition is proposed. The proposed method progresses through three steps: (i) a set of 2-D IMF features of various scale and orientation are extracted using DEMD, (ii) the (2-D) 2 LDA method is then applied to reduce the dimensionality of the feature space in both the row and column directions, and (iii) the nearest neighbor classifier is used for classification. We also propose two strategies for using the set of 2-D IMF features: ensemble DEMD vein representation (EDVR) and multichannel DEMD vein representation (MDVR). In experiments using palm vein databases, the proposed MDVR-based (2-D) 2 LDA method achieved recognition accuracy of 99.73%, thereby demonstrating its feasibility for palm vein recognition.

Paper Details

Date Published: 2 April 2014
PDF: 14 pages
Opt. Eng. 53(4) 043102 doi: 10.1117/1.OE.53.4.043102
Published in: Optical Engineering Volume 53, Issue 4
Show Author Affiliations
Jen-Chun Lee, Chinese Naval Academy (Taiwan)
Chien-Ping Chang, Chien Hsin Univ. of Science and Technology (Taiwan)
Wei-Kuei Chen, Chien Hsin Univ. of Science and Technology (Taiwan)

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