NDLI: Palm vein recognition using a high dynamic range approach

Content Provider	IET Digital Library
Author	Piciucco, Emanuela Maiorana, Emanuele Campisi, Patrizio
Abstract	In this study, the authors propose a novel approach for palm vein recognition relying on high dynamic range (HDR) imaging. Specifically, the authors speculate that the exploitation of multiple-exposure vein images guarantees better recognition performance than a baseline system relying on single-exposure acquisitions. To verify the authors’ assumptions, a multiple-exposure dataset is collected from 86 subjects, with 12 sets of palm vein images captured for each user. Each set is composed of five images, acquired at different exposures, which can be fused to generate a HDR representation of the actual vein pattern. Local binary pattern and local derivative pattern are employed to extract features from single-exposure images, raw HDR images, and tone-mapped HDR images. The obtained experimental results show that significant performance improvement can be achieved when discriminative features are extracted from HDR contents, with respect to the use of single-exposure images.
Starting Page	439
Ending Page	446
Page Count	8
ISSN	20474938
Volume Number	7
e-ISSN	20474946
Issue Number	Issue 5, Sep (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/7/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2017.0192
Journal	IET Biometrics
Publisher Date	2018-01-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Feature Extraction HDR Representation High Dynamic Range Approach High Dynamic Range Imaging Image Recognition Image Representation Multiple-exposure Dataset Multiple-exposure Vein Image Palm Vein Image Palm Vein Recognition Raw HDR Image Single-exposure Acquisitions Single-exposure Image Tone-mapped HDR Image Vein Recognition
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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