Iris segmentation is a process to isolate the accurate iris region from eye image for recognition. on non-ideal and noisy images with active contour. Nevertheless, it currently unclear how contour responds blurry or motion blur, which presents significant obstacle in segmentation. Investigation images, especially initial position, rarely published must be clarified. Moreover, evolution converge...

This paper discusses an analysis of human iris patterns for recognition of biometric system which consists of a segmentation system that is based on the Hough transform, and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region is then normalized into a rectangular block with constant dimensions to account for imaging...

A novel iris segmentation approach for noisy iris is proposed in this paper. The proposed approach comprises of specular reflection removal, pupil localization, iris localization and eyelid localization. Reflection map computation is devised to get the reflection ROI of eye image using adaptive threshold technique. Bilinear interpolation is used to fill these reflection points in the eye image....

Iris segmentation is a significant phase in the iris recognition process because errors cascade into all subsequent phases. Therefore, it important that are minimised. The U-Net architecture uses deep learning approach was previously adopted for this task, but its performance affected by deformation of images caused various noise factors unconstrained (non-ideal) environments. Scratches, blurri...

Extracting important features from an image is a complicated phase in the field of image processing, biometrics and computer vision. After crossing over phases such as denoising, segmentation and normalization in any pattern (Iris) Recognition System, feature extraction takes place which represents the features in the form of numerals or binary known as feature vector. In Iris Recognition Syste...

Iris patterns have been proven to be unique for each individual making them useful in human identification. Segmentation and feature extraction are crucial steps in matching one iris image with another. In this paper, iris segmentation is performed using Canny edge detection and the circular Hough transform. To overcome the pupil-iris center offset as well as iris stretching due to pupil dilati...

An iris recognition system uses the iris to distinguish the identity of a person using the rich iris texture feature. To effectively remove noise and precisely segment the stable iris region is a crucial stage prior to recognition. Most noises on iris images are caused by occlusion of eyelids or eyelashes in certain areas. In this paper, we propose an iris recognition system which precisely loc...

This paper discusses about Enhanced iris recognition which is used to overcome some of the problem like to automate the recognition of the iris by reducing complexity and increasing algorithm speed. Various challenges are faced while working with the iris recognition system. Iris recognition systems make use of the uniqueness of the iris patterns to derive a unique mapping. Iris recognition, as...

This paper describes a novel algorithm for exact eye contour detection in frontal face image. The exact eye shape is a useful piece of input information for applications like facial expression recognition, feature-based face recognition and face modelling. In contrast to well-known eye-segmentation methods, we do not rely on deformable models or image luminance gradient (edge) map. The eye wind...