Iris recognition and what is next? Iris diagnosis - a new challenging topic for machine vision from image acquisition to image interpretation
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Molecular image-based techniques are widely used in medicine to detect specific diseases. Look diagnosis is an important issue but also the analysis of the eye plays an important role in order to detect specific diseases. These topics are important topics in medicine and the standardization of these topics by an automatic system can be a new challenging field for machine vision. Compared to iris recognition has the iris diagnosis much more higher demands for the image acquisition and interpretation of the iris. One understands by iris diagnosis (Iridology) the investigation and analysis of the colored part of the eye, the iris, to discover factors, which play an important role for the prevention and treatment of illnesses, but also for the preservation of an optimum health. An automatic system would pave the way for a much wider use of the iris diagnosis for the diagnosis of illnesses and for the purpose of individual health protection. With this paper, we describe our work towards an automatic iris diagnosis system. We describe the image acquisition and the problems with it. Different ways are explained for image acquisition and image preprocessing. We describe the image analysis method for the detection of the iris. The meta-model for image interpretation is given. Based on this model we show the many tasks for image analysis that range from different image-object feature analysis, spatial image analysis to color image analysis. Our first results for the recognition of the iris are given. We describe how detecting the pupil and not wanted lamp spots. We explain how to recognize orange blue spots in the iris and match them against the topological map of the iris. Finally, we give an outlook for further work.Keywords:
Iris Recognition
IRIS (biosensor)
Feature (linguistics)
Iris recognition is the most reliable and accurate biometric identification system. Iris recognition system captures an image of an individual's eye, the iris in the image is segmented and normalized for extracting its feature. The performance of iris recognition systems depends on the process of segmentation of iris form the eye image. Segmentation is the most important part in iris recognition process because areas that are wrongly segmented out as iris regions will corrupt biometric templates resulting in very poor recognition. There are various methods for segmenting iris from eye image and give the best segmented result. In this paper, Daugman's method is used to find out the pupil and the iris boundaries. Here Iris images are taken from the CASIA Database, then the iris and pupil boundary are detected. By using Daughman's method the iris boundaries are segmented out. The computational time of segmentation by Daughman's method is less as it take very less time to segment the iris and pupilary boundaries of eye image and give appropriate value. Hence this method gives fast segmented output.
Iris Recognition
IRIS (biosensor)
Feature (linguistics)
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The automatic iris recognition has become one of the most important techniques for authenticating the identity of individuals. The analysis of human iris is a reliable tool for the individual authentication due to the iris structure. Iris patterns constitute one of the uniqueness, permanence, and performance biometric traits. Moreover, the iris is considered as not easily tampered biometric traits. Therefore, this paper considers investigating the common automated methods of iris recognition. It surveys the development of utilizing iris images as an authentication means through the explanation of the historical improvement of the processes of the iris analysis. The contribution of this paper is to provide readers with huge information collected and discussed from more than 40 papers of iris recognition studies which have been published in a period of more than 20 years.
Iris Recognition
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In iris recognition system,iris region is generally incomplete,many literatures have shown that iris feature have(uniqueness) for complete iris region.Free-feature number can indicate pattern-separability.In fact,the iris regions usually are incompletely in iris recognition process,even if there is part of iris region in some images.Whether or not there is pattern(uniqueness) in small iris regions is valuable studying for iris recognition.Research results show that there is still enough features in part of iris pattern and false reject rate will increase with iris regions decreasing.
Iris Recognition
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The overall performance of iris recognition systems is affected by the quality of acquired iris sample images. Due to the development of imaging technologies, visible wavelength iris recognition gained a lot of attention in the past few years. However, iris sample quality of unconstrained imaging conditions is a more challenging issue compared to the traditional near infrared iris biometrics. Therefore, measuring the quality of such iris images is essential in order to have good quality samples for iris recognition. In this paper, we investigate whether general purpose no-reference image quality metrics can assess visible wavelength iris sample quality.
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Iris recognition need user cooperate, but some factors is uncertainty and unstable between image-capture set and user little, the thing that iris information is imperfect in image captured is ubiquitous. According to the problem of imperfect iris recognition, the following issues have been studied in this paper. Firstly, the main steps of iris recognition are introduced, and then the relation between number of features and recognition rate is studied. By designing lots of experiments, iris classification correct rates are studied under the condition of different iris region's sizes. Results show that different parts of iris region all have importance for iris recognition and iris effective region' size will determine recognition accuracy.
Iris Recognition
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Iris recognition has been shown to be very accurate for human identification. We investigate the performance of the use of a partial iris for recognition. A partial iris identification system based on a one-dimensional approach to iris identification is developed. Experiment results show that a more distinguishable and individually unique signal is found in the inner rings of the iris. The results also show that it is possible to use only a portion of the iris for human identification.
Iris Recognition
IRIS (biosensor)
Identification
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Iris Recognition
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Eyelash
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Iris identification is one of the striking biometric identification procedure for recognizing human beings based on physical behaviour. The texture of iris is unique and its’ anatomy varies from individual to individual. The Iris recognition system works in three stages: in first phase the iris is l
IRIS (biosensor)
Iris Recognition
Identification
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The accuracy of Visible Wavelength (VW) iris recognition systems is significantly affected by the quality of iris sample images. Using degraded VW iris sample images can significantly decrease the performance of iris recognition systems. There are not many iris quality enhancement methods in the research field. Therefore, it is interesting to investigate whether existing image quality enhancement methods can improve the performance of biometric systems for degraded VW iris images. In this paper, we apply nine image enhancement methods to a VW iris database, which contains high quality and degraded iris sample images. The experimental results show that some of selected enhancement methods can significantly improve VW iris recognition system performance.
Iris Recognition
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Iris Recognition
IRIS (biosensor)
Identification
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