Sclera Segmentation using Spatial Kernel Fuzzy Clustering Methods

M. Maheshan, B. Harish, S. Kumar

Abstract

Biometrics is one of the domain that is gaining lot of importance in the present digital industry. Biometrics are getting integrated in different devices and reaching the end users at a very affordable cost. Among various biometric traits, Sclera is one such trait that is getting popular in the research community for its distinct nature of authenticating and identification of individuals. The recognition system using sclera trait purely depends on efficient segmentation of sclera image. Segmentation process is considered to be significant in image processing system because of better visualization. The segmentation can be done using region based, edge based, threshold based and also clustering based techniques. This paper concentrates on clustering based technique by proposing a variant of conventional Fuzzy C Means (FCM) algorithm. Though the Fuzzy C Means presents outstanding results in many applications, unfortunately it is sensitive to noise and ignore neighbourhood information. Thus to alleviate these limitations this paper presents Generalized Spatial Kernel Fuzzy C Means (GSK-FCM) clustering algorithms for sclera segmentation. To evaluate the proposed methods, experimentation are conducted on Sclera Segmentation and Recognition Benchmarking Competition (SSRBC 2015) dataset. The result of the experiments reveals that the proposed methods outperform the other variants of FCM.

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Paper Citation


in Harvard Style

Maheshan M., Harish B. and Kumar S. (2020). Sclera Segmentation using Spatial Kernel Fuzzy Clustering Methods.In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 433-439. DOI: 10.5220/0008935704330439


in Bibtex Style

@conference{icpram20,
author={M. Maheshan and B. Harish and S. Kumar},
title={Sclera Segmentation using Spatial Kernel Fuzzy Clustering Methods},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={433-439},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008935704330439},
isbn={978-989-758-397-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Sclera Segmentation using Spatial Kernel Fuzzy Clustering Methods
SN - 978-989-758-397-1
AU - Maheshan M.
AU - Harish B.
AU - Kumar S.
PY - 2020
SP - 433
EP - 439
DO - 10.5220/0008935704330439