Robust Iris Segmentation Method Based on a New Active Contour and Artificial Neural Networks

About this sample

About this sample


Words: 353 |

Page: 1|

2 min read

Published: Mar 1, 2019

Words: 353|Page: 1|2 min read

Published: Mar 1, 2019

Active contours or snakes are computer-generated curves that move within images to find object boundaries. They are often used in computer vision and image analysis to detect and locate objects, and to describe their shape. For example, a snake might be used edge detection, corner detection, motion tracking, and stereo matching; one might be used to find the outline of an organ in a medical image; or one might be used to automatically identify characters on a postal letter. Active contour model, also called snakes, is a framework in computer vision for delineating an object outline from a possibly noisy 2D image. The snakes model is popular in computer vision, and snakes are greatly used in applications like object tracking, shape recognition, segmentation, edge detection and stereo matching.

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A snake is an energy minimizing, deformable spline influenced by constraint and image forces that pull it towards object contours and internal forces that resist deformation. Snakes may be understood as a special case of the general technique of matching a deformable model to an image by means of energy minimization. In two dimensions, the active shape model represents a discrete version of this approach, taking advantage of the point distribution model to restrict the shape range to an explicit domain learned from a training set.

An additional force to the external force of the GVF active contour model is called the new active contour model. This force acts as a pressure force that pushes the contour to the object boundary. Without this pressure force, even if we have perfect edge detection the curve will shrink and vanish.

This new active contour model allows to move in both directions are expand and shrink which makes it adequate for various image segmentation applications including iris segmentation.

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New active contour model achieves the robust and accurate iris segmentation with high recognition rates for iris images for both pupil and iris appear to be noncircular, and there are several occlusions by eyelashes and eyelids. iris segmentation is done by a new active contour model after that SFTA feature extraction and ANN classification techniques are implemented scheme is discussed here after.

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Robust Iris Segmentation Method Based On a New Active Contour And Artificial Neural Networks. (2019, February 27). GradesFixer. Retrieved February 21, 2024, from
“Robust Iris Segmentation Method Based On a New Active Contour And Artificial Neural Networks.” GradesFixer, 27 Feb. 2019,
Robust Iris Segmentation Method Based On a New Active Contour And Artificial Neural Networks. [online]. Available at: <> [Accessed 21 Feb. 2024].
Robust Iris Segmentation Method Based On a New Active Contour And Artificial Neural Networks [Internet]. GradesFixer. 2019 Feb 27 [cited 2024 Feb 21]. Available from:
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