Palm vein recognition based on directional empirical mode decomposition

Jen-Chun Lee; Chien-Ping Chang; Wei-Kuei Chen

doi:10.1117/1.OE.53.4.043102

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2 April 2014 Palm vein recognition based on directional empirical mode decomposition

Jen-Chun Lee, Chien-Ping Chang, Wei-Kuei Chen

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Optical Engineering, 53(4), 043102 (2014). https://doi.org/10.1117/1.OE.53.4.043102

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.

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Jen-Chun Lee, Chien-Ping Chang, and Wei-Kuei Chen "Palm vein recognition based on directional empirical mode decomposition," Optical Engineering 53(4), 043102 (2 April 2014). https://doi.org/10.1117/1.OE.53.4.043102

Published: 2 April 2014

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