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Eyelid and eyelash segmentation based on wavelet transform for iris recognition

Authors: Mohammad Javad Aligholizadeh; Shahram Javadi; Reza Sabbaghi-Nadooshan; Kaveh Kangarloo;

Eyelid and eyelash segmentation based on wavelet transform for iris recognition

Abstract

Noise removal is a very important step in an iris segmentation process. Iris regions are usually occluded by Eyelid and eyelashes. For overcome this problem, we present a robust method for eyelid and eyelashes segmentation based on wavelet transform. Our approach follows two main stages. First, eyelashes are removed using wavelet transform. Then eyelids boundary are modeled with a parabolic curve. Second, Eyelashes are modeled by Hough transform. Afterwards eyelashes are segmented using neural network. Experimental results on a set of 756 images show that the accuracy of proposed method leading to accurate eyelid and eyelash segmentation.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Average
Top 10%
Average
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