Iris Recognition for Biometric Personal Identification Using Neural Networks.
ABSTRACT This paper presents iris recognition for personal identification using neural networks. Iris recognition system consists of localization of the iris region and generation of data set of iris images and then iris pattern recognition. One of the problems in iris recognition is fast and accurate localization of the iris image. In this paper, fast algorithm is used for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement it is represented by a data set. Using this data set a neural network is applied for the classification of iris patterns. Results of simulations illustrate the effectiveness of the neural system in personal identification.
- SourceAvailable from: Fatma zohra Chelali[Show description] [Hide description]
DESCRIPTION: This article describes the use of circular Hough transform to detect iris.
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ABSTRACT: Abstract This article presents a robust method for detecting iris features in frontal face images based on circular Hough transform. The software of the application is based on detecting the circles surrounding the exterior iris pattern from a set of facial images in different color spaces. The circular Hough transform is used for this purpose. First an edge detection technique is used for finding the edges in the input image. After that the characteristic points of circles are determined, after which the pattern of the iris is extracted. Good results are obtained in different color spaces.Journal of Computer Science and Technology 09/2012; 2(5):114-121. · 0.64 Impact Factor
Conference Paper: Adaptive Iris Segmentation.[Show abstract] [Hide abstract]
ABSTRACT: In this paper an adaptive iris segmentation algorithm is presented. In the proposed algorithm Otsu Threshold value, average gray level of the image, image size, Hough-Circle search are used for adaptive segmentation of irises. Otsu threshold is used for selecting threshold value in order to determine pupil location. Then Hough circle is utilized for pupillary boundary, and finally gradient search is used for the limbic boundary detection. The algorithm achieved 98% segmentation rate in batch processing of the CASIA version 1 (756 Images) and version 3 (CASIA-IrisV3-Interval, 2655 Images) databases.Advances in Information Security and Assurance, Third International Conference and Workshops, ISA 2009, Seoul, Korea, June 25-27, 2009. Proceedings; 01/2009