Superpixel-based Zernike moments for palm-print recognition
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Date
2019
Authors
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Journal ISSN
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Publisher
Université de M'sila
Abstract
In the contemporary period, significant attention has been focused
on the prospects of innovative personal recognition methods based on
palm-print biometrics. However, diminished local consistency and interference
from noise are only some of the obstacles that hinder the most common
methods of palm-print imaging such as the grey texture and other low-level of
the palm. Nevertheless, the development of the process and tackling of the
obstacles faced have a potential solution in the form of high-level characteristic
imaging for palm-print identification. In this study, Zernike moments are used
for acquiring superpixel features that are spiral scanned images, which is an
innovative recognition method. By using the extreme learning machine, the
inter- and intra-similarities of the palm-print feature maps are determined. Our
experiments yield good results with an accuracy rate of 97.52 and an equal
error rate of 1.47% on the palm-print PolyU database.
Description
Keywords
palm-print recognition; image segmentation; feature extraction; extreme learning machine; ELM; image matching.