Open AccessEfficient approach for non‐ideal iris segmentation using improved particle swarm optimisation‐based multilevel thresholding and geodesic active contours
Author(s) -
Rapaka Satish,
Kumar Pullakura Rajesh
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2016.0917
Subject(s) - thresholding , artificial intelligence , segmentation , computer science , iris (biosensor) , computer vision , iris recognition , image segmentation , geodesic , active contour model , pattern recognition (psychology) , noise (video) , scale space segmentation , particle swarm optimization , image (mathematics) , mathematics , biometrics , algorithm , mathematical analysis
Segmentation is an important step in iris recognition framework because the accuracy of the iris recognition system is affected by the segmentation of the iris. The image acquisition introduces noise artefacts such as specular reflections, eyelids/eyelashes occlusions and overlapping intensities, which makes the segmentation process difficult. An efficient method has been proposed for the segmentation of iris images that deal with non‐circular iris boundaries and other noise artefacts mentioned above. The proposed method uses the Otsu multilevel thresholding based on improved particle swarm optimisation technique as a pre‐segmentation step. Pre‐segmentation step delimits the iris region from the other parts of an eye image. The geodesic active contours incorporated with a novel stopping function is then used to segment non‐circular iris boundaries. The recognition accuracy of the proposed method is verified using the standard databases, CASIA v3 Interval and UBIRISv1. Obtained results have been compared with existing methods and have an encouraging performance.