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This paper presents a survey of literature related to the one of the biometric recognition systems-iris recognition system. Biometric authentication has become one of the important security technologies due to the prominent properties of biometrics compared to other authentication methods. Since most of the phenotypes of humans are unique, physiolo...
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... is a thin, circular structure in the eye, which is responsible for controlling the size of the pupil. It mainly consists of few components as Fig.1 shows. ...
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... the fuzzified image enhancement can help the training process and training outcome (accuracy), and ultimately better adapt. Fig. 9. Results of [11] Unlike other research proposals, [12] has completely took the advantage of prominent features of Convolutional Neural Networks to accurately and efficiently do the segmentation process. According to Fig. 10, proposed method has shown lower EER rate than other algorithms even being used in commercial products such as IriCore. ...
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... direction and iris boundary along direction. In this method iris region can be mapped to a single plane sheet to allow comparisons. Also, have to note that this rubber sheet model does not compensate for rotation variance but for pupil dilation, image distance inconsistencies and non-centric pupil displacements. In the normalized iris image as in Fig. 11, r's direction will call as radial direction and direction of as angular direction. Still almost all researches use rubber-sheet model for iris normalization. But there are other researches carried out to find new ...
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... Image Enhancement: Low contrast of the image as well as non-uniform illumination may cause poor performance in Fig. 12. Verification results of Image registration and Rubber Sheet method [14] feature extraction stage. To avoid these factors normalized image must be enhanced using different techniques to compensate. One of the techniques is using Local histogram analysis which make uniformly illumination image with rich detailed normalized image [15], ...
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... of the techniques is using Local histogram analysis which make uniformly illumination image with rich detailed normalized image [15], [16]. Before enhancing the image Fig.13 (a) details can identify poorly. ...
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... 0 ,y 0 ) denotes the location in the image and denotes effective width and length [4]. Each pattern separates to extract its information use these two Gabor filters. As shown in Fig.15, 2D iris pattern is divided into several 1D signals and then 1D signal convolve with 1D Gabor filter to get the response. Using Odd and even symmetric Gabor filters can obtain real response and imaginary response and then this phase information quantifies into four possible quadrant levels in complex plane. Four can be represented in 2 ...
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... filter to get the response. Using Odd and even symmetric Gabor filters can obtain real response and imaginary response and then this phase information quantifies into four possible quadrant levels in complex plane. Four can be represented in 2 bits. So, there are 2 bits to represent levels. Each pixel of normalized iris image represents by 2 Fig. 15. Process of Feature Extraction using Gabor Filter [4] bits in iris template and generate 2,048 bits for the template. Also, masking bits will be generated for corrupted areas of iris pattern. Noise area intensities are calculated by averaging intensity levels of near bits to minimize influence. Generally, this creates 265 bytes of ...
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... center pixel value will be calculated multiplying the converted neighborhood pixel values with weights to 2 n [24]. [24] After this LBP operation, as Fig.18 shows, chunk feature encoder will be invoked and feature code will be generated by going through top to bottom, from left to right in all defined blocks. ...
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... mentions that experimental results has shown that this algorithm can get higher recognition rate than the Fig. 18. Generation of the iris code [24] traditional iris feature extraction method. According to 19, even though Daugman has 0.01% higher Correct recognition rate than proposed method by [24], still ERR of proposed method is significantly low. Fig. 19. Comparison of recognition accuracy of various recognition schemes [24] 6) Convolutional ...
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... that experimental results has shown that this algorithm can get higher recognition rate than the Fig. 18. Generation of the iris code [24] traditional iris feature extraction method. According to 19, even though Daugman has 0.01% higher Correct recognition rate than proposed method by [24], still ERR of proposed method is significantly low. Fig. 19. Comparison of recognition accuracy of various recognition schemes [24] 6) Convolutional Neural Network: A study has provided an experimental approach to use deep convolutional features for iris recognition. Specifically tend to use this for feature extraction. Approach is using VGG-Net for iris recognition task [25]. Treating model as ...
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... for the evaluation of performance of the VGG-Net for this task has shown high accuracy rate in Fig. 21 for any level after 7th layer, with 98% of minimum accuracy. Also, there is a drop of accuracy after level 11 and one reason for that could be when the neural network layers identify abstract data which do not discriminate iris patterns from one another as suggested in [25]. E. Template Matching After features being extracted and ...
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Over the years Biometric authentication system
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Citations
... In the following part, we evaluate these reviews and point out how our work differs from theirs. [8][9][10] summarize the basic IR process according to the traditional biometric recognition workflow, including image acquisition, pre-processing, image segmentation, feature extraction, and classification. In [8,9], in addition to these above steps, image normalization is also introduced. ...
... [8][9][10] summarize the basic IR process according to the traditional biometric recognition workflow, including image acquisition, pre-processing, image segmentation, feature extraction, and classification. In [8,9], in addition to these above steps, image normalization is also introduced. Meanwhile, these reviews introduce machine learning-based approaches instead of focusing on deep learning methods, which cannot provide a comprehensive insight into the current deep learning-based mainstream. ...
... Meanwhile, these reviews introduce machine learning-based approaches instead of focusing on deep learning methods, which cannot provide a comprehensive insight into the current deep learning-based mainstream. Specifically, [8] mentions neural network techniques in the feature extraction and classification phases, and [9] briefly summarizes the application of CNNs in the iris image segmentation and feature extraction phases, but there is not a comprehensive and systematic summary of deep learning techniques. Additionally, reviews [9,10] lack the summary of influential public IR datasets. ...
Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional machine learning-based iris recognition methods, deep learning technology does not rely on feature engineering and boasts excellent performance. This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning. We first introduce the background of iris recognition and the motivation and contribution of this survey. Then, we present the common datasets widely used in iris recognition. After that, we summarize the key tasks involved in the process of iris recognition based on deep learning technology, including identification, segmentation, presentation attack detection, and localization. Finally, we discuss the challenges and potential development of iris recognition. This review provides a comprehensive sight of the research of iris recognition based on deep learning.
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An iris recognition system for person identification is
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and two-direction scanning methods. For limbic boundary
detection, Wildes method is modified by restricting the process of
Canny edge detector and Hough transform to a small Region-Of-
Interest (ROI) not exceeding 20% of the image size. For eyelid
detection, the method of Refine-Connect-Extend-Smooth (R-C-ES)
is used, which detect three possible cases (single eyelid, both
eyelids and free iris). For iris normalization, rubber-sheet model
transform is used and for iris coding Gabor filter is used. The
performance of the system is evaluated for the individual stages
and for the whole system using three different databases (CASIAV1.0,
CASIA-V4.0-Lamp and SDUMLA-HMT). The accuracy of
correct detection reached 99.9%-100% for pupil boundary and
99.6%-99.9% for limbic boundary detection. For eyelid detection;
the accuracy reached 93.2%-97.6% for upper eyelid, 95.3%-
99.15% for lower eyelid and 96.7%-96.92% for free iris (iris not
occluded by eyelids). The overall accuracy and the Equal Error
Rate (EER) of the system for CASIA-V1.0 database are 96.48%
and 1.76%, for CASIA-V4.0-Lamp, are 95.1% and 2.45%, and for
SDUMLA-HMT are 93.6% and 3.2%.
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Abstract
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applications in the field of several industries. An explicit Finite difference method is applied
for the solution of governing equations. By considering governing equations of the
mathematical model as a platform non-linier differential equation have been reduced to
ordinary differential equations by using similarity transformations. BVP4C software
technique is used for finding the solutions and results are presented in the form of tables and
graphs for several equations. After simulation, it is found that the heat transfer rate decreases
with higher values of the magnetic field as well as the radiation. The temperature profile and
velocity profile of the Casson fluid flow is directly related to several parameters. Finally, it is
observed that several parameters such as Casson fluid parameter, radiation parameter, Prandtl
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Iris Recognition Technology is a biometric method that takes an image of an eye pattern, converts the image into a binary template and then saves the data on a server for future matching. Iris cameras are used to learn the unique identity of an individual by analyzing unique random patterns that appear clearly inside the eye from a certain distance. It uses multiple techniques including optics, statistical inferences, pattern recognition and many more. Because the market is primarily driven by the increasing demand for security systems, some information about the market for this technology and future predictions has been presented in this paper.
