Iris Recognition for Biometric Personal Identification Using Neural Networks

Conference Paper · September 2007with8 Reads
DOI: 10.1007/978-3-540-74695-9_57 · Source: DBLP
Conference: Artificial Neural Networks - ICANN 2007, 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part II
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.
    • "Using determined inner and outer radiuses the iris region is detected. The application of the Hough transform needs long time to locate the boundaries of the iris[6]. The aim of this work is to realize an application wh ich detects the iris pattern fro m an eye image. "
    [Show abstract] [Hide abstract] ABSTRACT: Th is article p resents a robust method for detecting iris features in frontal face images based on circular Hough transform. The software of the applicat ion is based on detecting the circles surrounding the exterio r iris pattern from a set of facial images in d ifferent 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.
    Full-text · Article · Dec 2012
    • "Using determined inner and outer radiuses the iris region is detected. The application of the Hough transform needs long time to locate the boundaries of the iris[6]. The aim of this work is to realize an application wh ich detects the iris pattern fro m an eye image. "
    [Show abstract] [Hide abstract] 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.
    Full-text · Article · Sep 2012
    • "Detailed discussions on the issues related to performance of segmentation methods can be found in [17], and in [18]. A categorization along with the references on the iris segmentation can be found in [19, 20]. Iris localization generally takes nearly more than half of the total processing time in the recognition systems, as pointed in [18]. "
    [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.
    Full-text · Conference Paper · Jun 2009 · Journal of Computer Science and Technology
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