NDLI: Efficient method for segmentation of noisy and non-circular iris images using improved particle swarm optimisation-based MRFCM

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Efficient method for segmentation of noisy and non-circular iris images using improved particle swarm optimisation-based MRFCM

Content Provider	IET Digital Library
Author	Satish, Rapaka Kumar, P. Rajesh
Abstract	Segmentation of the iris is a crucial stage in an automated iris-based recognition system. The performance of any biometric system primarily relies on how effectively the iris is extracted from the unwanted parts of an iris image. The process of iris segmentation is mainly affected by the noise artefacts such as eyelid/eyelashes occlusions, specular reflections, intensity inhomogeneities, and non-circularity of the iris boundary. A novel and an efficient method has been proposed in this work to segment noisy and non-circular iris boundaries. The mathematical modelling of morphological reconstruct fuzzy C-means clustering (MRFCM) has been presented. The MRFCM based on improved particle swarm optimisation has been implemented before the segmentation in the recognition framework. The resultant images are then segmented by employing geodesic active contours incorporated by a new stopping function. The effect of the proposed segmentation method on iris recognition is observed through matching score distribution. The popular and publicly available datasets such as UBIRISv1, CASIA-v3-Interval, MMU1, and Mobile Iris Challenge Evaluation databases are considered for the evaluation of the proposed method. Recognition accuracy is validated and compared with the well-existing methods.
Starting Page	78
Ending Page	90
Page Count	13
ISSN	20474938
Volume Number	9
e-ISSN	20474946
Issue Number	Issue 2, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/9/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2019.0026
Journal	IET Biometrics
Publisher Date	2019-11-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automated Iris-based Recognition System Biometric System Combinatorial Mathematics Computer Vision And Image Processing Technique Data Security Evaluation Database Feature Extraction Fuzzy Set Theory Geodesic Active Contours Image Recognition Image Reconstruction Image Segmentation Improved Particle Swarm Optimisation Improved Particle Swarm Optimisation-based MRFCM Iris Boundary Iris Recognition Iris Segmentation Method Matching Score Distribution Mathematical Model Morphological Reconstruct Fuzzy C-means Clustering MRFCM Noise Artefacts Noncircular Iris Boundaries Noncircular Iris Image Optimisation Technique Particle Swarm Optimisation Pattern Clustering Recognition Framework Stopping Function
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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