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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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Iris segmentation plays a pivotal role in the iris recognition system. The deep learning technique developed in recent years has gradually been applied to iris recognition techniques. As we all know, applying deep learning techniques requires a large number of data sets with high-quality manual labels. The larger the amount of data, the better the...
This paper proposes the first known to us iris recognition methodology designed specifically for post-mortem samples. We propose to use deep learning-based iris segmentation models to extract highly irregular iris texture areas in post-mortem iris images. We show how to use segmentation masks predicted by neural networks in conventional, Gabor-base...
Biometric authentication methods, which are based on human physiological or behavioral characteristics,
offer alternatives that are less susceptible to these threats. Some multi-factor authentication schemes utilize
fingerprint scan or iris scan biometric techniques, but both of these are physiological characteristics, not
behavioral. In our projec...
The recent growth of digital transactions over the internet is high. This is the newest topic which is to be implemented as well as need to study related with the security. The development of technology we need to improve and build the safe authentication scheme over the internet. The safe authentication scheme is protecting the user valuable infor...
Biometric authentication is, over time, becoming an indispensable complementary component to traditional authentication methods that use passwords and tokens. As a result, the research interest in the protection techniques for the biometric template has also grown considerably. In this paper, we present a light-weight AI-based biometric authenticat...
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.
... Recently, Convolutional Neural Networks (CNN) approach was introduced as one competitive approach iris recognition, [47]. The most recent works that have used the approach of CNN for iris recognition were [48]- [51]. ...
An iris recognition system for person identification is
developed with a new method for iris localization. For pupil
boundary detection, a method robust to the specular point
reflection problem is developed. It consists of morphological filter
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%.
EFFECT OF SOLAR THERMAL RADIATION AND MAGNETIC FIELD ON THE FLOW OF CASSON FLUID
Abstract
In this paper, the behavior of the non-Newtonian fluid and heat transfer is considered in an
exponentially stretching surface in the occurrence of porous magnetic field, as it has vast
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
number, and Eckert number are stable at the point of convergence.
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.
