NDLI: Robust and adaptive algorithm for hyperspectral palmprint region of interest extraction

Content Provider	IET Digital Library
Author	Zhao, Shuping Zhang, Bob
Abstract	Recently, hyperspectral imaging has attracted more and more considerable research attention because of its discriminative information. This study proposes a robust approach to adaptively extract the hyperspectral palmprint region of interest (ROI) captured by a hyperspectral palmprint acquisition device, which is considered one of the most important stages in palmprint recognition. For different spectral wavelengths, the image has different illuminations and unbalanced shadows. In particular, mean grey values of palm images in different bands have large variations, such that binarisation of the palm image can be considered a challenging task to accurately separate the contour of the palm from the original image. To solve these problems, this study proposes an adaptive ROI segmentation algorithm, whereby a support vector machine-based method is used to detect the palm from the image and a coordinate established to ensure the accuracy of the ROI. The proposed method has been tested on a hyperspectral palm data set which covers spectrums from 530–1030 nm with 20 nm intervals. The experimental results showed that the proposed algorithm is effective and efficient at locating the ROI in hyperspectral palmprint images, where local binary pattern features were extracted from the ROIs achieving an equal error rate (EER) of 1.49% and an accuracy of 99.51% in recognition.
Starting Page	391
Ending Page	400
Page Count	10
ISSN	20474938
Volume Number	8
e-ISSN	20474946
Issue Number	Issue 6, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/8/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2018.5051
Journal	IET Biometrics
Publisher Date	2019-05-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive ROI Segmentation Algorithm Computer Vision And Image Processing Technique EER Efficiency 1.49 Percent Efficiency 99.51 Percent Feature Extraction Hyperspectral Image Hyperspectral Palm Data Set Hyperspectral Palmprint Acquisition Device Hyperspectral Palmprint Imaging Hyperspectral Palmprint Region of Interest Extraction Image Colour Analysis Image Recognition Image Segmentation Knowledge Engineering Technique Local Binary Pattern Feature Extraction Mean Grey Value Palm Image Binarisation Palmprint Recognition Support Vector Machine Support Vector Machine-based Method Wavelength 20.0 Nm Wavelength 530.0 Nm to 1030.0 Nm
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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