Biometrics has become important in security applications. In comparison with many other
biometric features, iris recognition has very high recognition accuracy because it depends on
iris which is located in a place that still stable throughout human life and the probability to find
two identical iris's is close to zero. The identification system consists of several stages including
segmentation stage which is the most serious and critical one. The current segmentation
methods still have limitation in localizing the iris due to circular shape consideration of the
pupil. In this research, Daugman method is done to investigate the segmentation techniques.
Eyelid detection is another step that has been included in this study as a part of segmentation
stage to localize the iris accurately and remove unwanted area that might be included. The
obtained iris region is encoded using haar wavelets to construct the iris code, which contains
the most discriminating feature in the iris pattern. Hamming distance is used for comparison of
iris templates in the recognition stage. The dataset which is used for the study is UBIRIS
database. A comparative study of different edge detector operator is performed. It is observed
that canny operator is best suited to extract most of the edges to generate the iris code for
comparison. Recognition rate of 89% and rejection rate of 95% is achieved.
Iris recognition is an automated method of bio metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance.
Retinal scanning is a different, ocular-based bio metric technology that uses the unique patterns on a person's retina blood vessels and is often confused with iris recognition. Iris recognition uses video camera technology with subtle near infrared illumination to acquire images of the detail-rich, intricate structures of the iris which are visible externally.
A study of Iris Recognition technology over the in use biometric technologies these days. These Study shows how beneficial the iris technology can be to the Human in future.
I have put all my efforts in this study and have made an simple easy to understand ppt.
in terms of Forensic Science, how iris recognition is done and what are the key factors that should be kept in mind. It can be its Advantages, Disadvantages, Approaches and very importantly the working process.
iris recognition system as means of unique identification Being Topper
Project Done and submitted by Students Of final year CBP Government Engineering College
student name : vipin kumar khutail , Krishnanad Mishra , Jaswant kumar, Rahul Vashisht
Project Description :
Iris recognition is an automated method of bio-metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex random patterns are unique, stable, and can be seen from some distance
IRIS Recognition is Fast Developing to be a Fool Proof And Fast Identification Technique. It is a classic Biometrics Application that is in an Advanced stage of Research all Over the world.
An Intelligent System for Secured Authentication using Hierarchical Visual Cr...IDES Editor
This paper introduces the idea of hierarchical visual
cryptography. Authentication is the important issue over the
internet. This paper describes a secured authentication
mechanism with the help of visual cryptography. Visual
cryptography simply divides secret information in to number
of parts called shares. These shares are further transmitted
over the network and at the receiving end secrets are revealed
by superimposition. Many layers of visual cryptography exist
in proposed system hence called hierarchical visual
cryptography. Remote voting systems now a day’s widely using
visual cryptography for authentication purpose.
Abstract— The division is the urgent stage in iris acknowledgment. We have utilized the worldwide limit an incentive for division. In the above calculation we have not considered the eyelid and eyelashes relics, which corrupt the execution of iris acknowledgment framework. The framework gives sufficient execution likewise the outcomes are attractive. Assist advancement of this technique is under way and the outcomes will be accounted for sooner rather than later. Based on the reasonable peculiarity of the iris designs we can anticipate that iris acknowledgment framework will turn into the main innovation in personality verification.In this paper, iris acknowledgment calculation is depicted. As innovation advances and data and scholarly properties are needed by numerous unapproved work force. Therefore numerous associations have being scanning routes for more secure confirmation strategies for the client get to. The framework steps are catching iris designs; deciding the area of iris limits; changing over the iris limit to the binarized picture; The framework has been actualized and tried utilizing dataset of number of tests of iris information with various complexity quality.
Keywords— GAC, Iris Recognition, Iris Segmentation, Snakes.
Iris recognition is an automated method of bio metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance.
Retinal scanning is a different, ocular-based bio metric technology that uses the unique patterns on a person's retina blood vessels and is often confused with iris recognition. Iris recognition uses video camera technology with subtle near infrared illumination to acquire images of the detail-rich, intricate structures of the iris which are visible externally.
A study of Iris Recognition technology over the in use biometric technologies these days. These Study shows how beneficial the iris technology can be to the Human in future.
I have put all my efforts in this study and have made an simple easy to understand ppt.
in terms of Forensic Science, how iris recognition is done and what are the key factors that should be kept in mind. It can be its Advantages, Disadvantages, Approaches and very importantly the working process.
iris recognition system as means of unique identification Being Topper
Project Done and submitted by Students Of final year CBP Government Engineering College
student name : vipin kumar khutail , Krishnanad Mishra , Jaswant kumar, Rahul Vashisht
Project Description :
Iris recognition is an automated method of bio-metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex random patterns are unique, stable, and can be seen from some distance
IRIS Recognition is Fast Developing to be a Fool Proof And Fast Identification Technique. It is a classic Biometrics Application that is in an Advanced stage of Research all Over the world.
An Intelligent System for Secured Authentication using Hierarchical Visual Cr...IDES Editor
This paper introduces the idea of hierarchical visual
cryptography. Authentication is the important issue over the
internet. This paper describes a secured authentication
mechanism with the help of visual cryptography. Visual
cryptography simply divides secret information in to number
of parts called shares. These shares are further transmitted
over the network and at the receiving end secrets are revealed
by superimposition. Many layers of visual cryptography exist
in proposed system hence called hierarchical visual
cryptography. Remote voting systems now a day’s widely using
visual cryptography for authentication purpose.
Abstract— The division is the urgent stage in iris acknowledgment. We have utilized the worldwide limit an incentive for division. In the above calculation we have not considered the eyelid and eyelashes relics, which corrupt the execution of iris acknowledgment framework. The framework gives sufficient execution likewise the outcomes are attractive. Assist advancement of this technique is under way and the outcomes will be accounted for sooner rather than later. Based on the reasonable peculiarity of the iris designs we can anticipate that iris acknowledgment framework will turn into the main innovation in personality verification.In this paper, iris acknowledgment calculation is depicted. As innovation advances and data and scholarly properties are needed by numerous unapproved work force. Therefore numerous associations have being scanning routes for more secure confirmation strategies for the client get to. The framework steps are catching iris designs; deciding the area of iris limits; changing over the iris limit to the binarized picture; The framework has been actualized and tried utilizing dataset of number of tests of iris information with various complexity quality.
Keywords— GAC, Iris Recognition, Iris Segmentation, Snakes.
Mobile Voting System Using Advanced NFC Technologyijsrd.com
Electronic voting system are becoming popular with wide spread use of computer and embedded system. Security is the main important issue should be considered in such system. Mobile voting system is basically used for collecting and counting votes. In this technology include punch card, optical scan voting system. This paper proposes a new Mobile based voting system using advanced NFC technology, Voting is the process that allows the general public or the people to choose their leaders and articulate views on how they will be governed. This gives a comprehensive analysis of security with respect to NFC. This study deals with the use of information technology to handle electoral processes starting from voters and candidates registration to the actual casting and counting of ballots. Exploring mobile voting from a systems perspective can demonstrate the attributes of the current systems and the possible solutions for the voting process so any one can caste their vote from any place.
Ever feel confined when you’re plugged in and stuck at a desktop? Well with the invention of broadband wireless Internet the accessibility and mobility is boundless. The presentation will look at the differences between wireless and plugged-in Internet, as well as how this Wireless connection has impacted urban spaces and our social life. [Click the image]
Comparative Analysis of Power System Stabilizer using Artificial Intelligence...ijsrd.com
Power system stabilizers (PSSs) are used to enhance the damping during low frequency oscillations. The paper presents study of power system stabilizer using fuzzy logic and neural network to enhance stability of single machine infinite bus system. In this paper basic problem of conventional power system stabilizer for stability enhancement is defined which is traditionally used. Artificial intelligence techniques provide one alternative for stability enhancement and speed deviation (Δw). The proposed method using Artificial intelligence techniques achieves better improvement than conventional power system stabilizer. Fuzzy logic rules were developed for triangular membership function of input and output variables. Neuro controller is implemented and it is compared with reference model. The system is simulated in SIMULINK environment and the performances of conventional, Fuzzy based and Neural network based power system stabilizers are compared.
Power Quality Enhancement in Power Distribution system using Artificial intel...sundar balan
Dynamic voltage Restorer
Artificial intelligence based Dynamic voltage restorer
DVR
Artificial neural network based DVR dynamic voltage restorer
Harmonics voltage harmonics voltage sag voltage swell
Power Quality Enhancement in Power Distribution system using Artificial intelligence based Dynamic Voltage Restorer
This report will give idea of key steps in developing an algorithm for \’Iris based Recognition system\’.Experimental observations as well are also shown.
Wireless LAN Security, Policy, and Deployment Best PracticesCisco Mobility
The current state of wireless security, covering wireless device access, preventing rogue threats and addressing wireless attacks. Special focus on device profiling and policy covering how to prevent unauthorized (such as smartphones and tablets) from accessing the network. Learn More: http://www.cisco.com/go/wireless
EFFECTIVENESS OF FEATURE DETECTION OPERATORS ON THE PERFORMANCE OF IRIS BIOME...IJNSA Journal
Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed systems, make it a good candidate to replace most of thesecurity systems around. By making use of the distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person. Identification of this person is possible by applying appropriate matching algorithm.In this paper, Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical analysis of different feature detection operators is performed, features extracted is encoded using Haar wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and False Reject Rate is 10%.
EFFECTIVENESS OF FEATURE DETECTION OPERATORS ON THE PERFORMANCE OF IRIS BIOME...IJNSA Journal
Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the
security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed
systems, make it a good candidate to replace most of thesecurity systems around. By making use of the
distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person.
Identification of this person is possible by applying appropriate matching algorithm.In this paper,
Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical
analysis of different feature detection operators is performed, features extracted is encoded using Haar
wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on
the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of
the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and
False Reject Rate is 10%.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Efficient Small Template Iris Recognition System Using Wavelet TransformCSCJournals
Iris recognition is known as an inherently reliable biometric technique for human identification. Feature extraction is a crucial step in iris recognition, and the trend nowadays is to reduce the size of the extracted features. Special efforts have been applied in order to obtain low templates size and fast verification algorithms. These efforts are intended to enable a human authentication in small embedded systems, such as an Integrated Circuit smart card. In this paper, an effective eyelids removing method, based on masking the iris, has been applied. Moreover, an efficient iris recognition encoding algorithm has been employed. Different combination of wavelet coefficients which quantized with multiple quantization levels are used and the best wavelet coefficients and quantization levels are determined. The system is based on an empirical analysis of CASIA iris database images. Experimental results show that this algorithm is efficient and gives promising results of False Accept Ratio (FAR) = 0% and False Reject Ratio (FRR) = 1% with a template size of only 364 bits.
WAVELET PACKET BASED IRIS TEXTURE ANALYSIS FOR PERSON AUTHENTICATIONsipij
There is considerable rise in the research of iris recognition system over a period of time. Most of the
researchers has been focused on the development of new iris pre-processing and recognition algorithms for
good quail iris images. In this paper, iris recognition system using Haar wavelet packet is presented.
Wavelet Packet Transform (WPT ) which is extension of discrete wavelet transform has multi-resolution
approach. In this iris information is encoded based on energy of wavelet packets.. Our proposed work
significantly decreases the error rate in recognition of noisy images. A comparison of this work with nonorthogonal Gabor wavelets method is done. Computational complexity of our work is also less as
compared to Gabor wavelets method.
Iris recognition for personal identification using lamstar neural networkijcsit
One of the promising biometric recognition method is Iris recognition. This is because the iris texture provides many features such as freckles, coronas, stripes, furrows, crypts, etc. Those features are unique for different people and distinguishable. Such unique features in the anatomical structure of the iris make it
possible the differentiation among individuals. So during last year’s huge number of people have been
trying to improve its performance. In this article first different common steps for the Iris recognition system
is explained. Then a special type of neural network is used for recognition part. Experimental results show high accuracy can be obtained especially when the primary steps are done well.
An exploration of periocular region with reduced region for authentication re...csandit
Biometrics is science of measuring and statistically analyzing biological data. Biometric system
establishes identity of a person based on unique physical or behavioral characteristic possessed
by an individual. Behavioral biometrics measures characteristics which are acquired naturally
over time. Physical biometrics measures inherent physical characteristics on an individual.
Over the last few decades enormous attention is drawn towards ocular biometrics. Cues
provided by ocular region have led to exploration of newer traits. Feasibility of periocular
region as a useful biometric trait has been explored recently. With the promising results of
preliminary examination, research towards periocular region is currently gaining lot of
prominence. Researchers have analyzed various techniques of feature extraction and
classification in the periocular region. This paper investigates the effect of using Lower Central
Periocular Region (LCPR) for identification. The results obtained are comparable with those
acquired for full periocular texture features with an advantage of reduced periocular area.
AN EXPLORATION OF PERIOCULAR REGION WITH REDUCED REGION FOR AUTHENTICATION : ...cscpconf
Biometrics is science of measuring and statistically analyzing biological data. Biometric system establishes identity of a person based on unique physical or behavioral characteristic possessed by an individual. Behavioral biometrics measures characteristics which are acquired naturally over time. Physical biometrics measures inherent physical characteristics on an individual. Over the last few decades enormous attention is drawn towards ocular biometrics. Cues provided by ocular region have led to exploration of newer traits. Feasibility of periocular region as a useful biometric trait has been explored recently. With the promising results of preliminary examination, research towards periocular region is currently gaining lot of
prominence. Researchers have analyzed various techniques of feature extraction and classification in the periocular region. This paper investigates the effect of using Lower Central
Periocular Region (LCPR) for identification. The results obtained are comparable with those acquired for full periocular texture features with an advantage of reduced periocular area
A robust iris recognition method on adverse conditionsijcseit
As a stable biometric system, iris has recently attracted great attention among the researchers. However,
research is still needed to provide appropriate solutions to ensure the resistance of the system against error
factors. The present study has tried to apply a mask to the image so that the unexpected factors affecting
the location of the iris can be removed. So, pupil localization will be faster and robust. Then to locate the
exact location of the iris, a simple stage of boundary displacement due to the Canny edge detector has been
applied. Then, with searching left and right IRIS edge point, outer radios of IRIS will be detect. Through
the process of extracting the iris features, it has been sought to obtain the distinctive iris texture features by
using a discrete stationary wavelets transform 2-D (DSWT2). Using DSWT2 tool and symlet 4 wavelet,
distinctive features are extracted. To reduce the computational cost, the features obtained from the
application of the wavelet have been investigated and a feature selection procedure, using similarity
criteria, has been implemented. Finally, the iris matching has been performed using a semi-correlation
criterion. The accuracy of the proposed method for localization on CASIA-v1, CASIA-v3 is 99.73%,
98.24% and 97.04%, respectively. The accuracy of the feature extraction proposed method for CASIA3 iris
images database is 97.82%, which confirms the efficiency of the proposed method.
A Survey : Iris Based Recognition SystemsEditor IJMTER
The security is one of the important aspect of today's life. Iris recognization is one of the leading
research of security which is used to identify the individual person. Usually iris based biometric is more better
than other biometric in terms of accuracy, fast, stability, uniqueness. The iris recognition system works by
capturing and storing biometric information and then compare scanned copy of iris biometric with the stored iris
images in the database. There are several Iris Based Recognition Systems are developed so far. In this paper we
presented several iris techniques and create a base for our future roadmap.
Iris recognition systems have attracted much attention for their uniqueness, stability and reliability. However, performance of this system depends on quality of iris image. Therefore there is a need to select good quality images before features can be extracted. In this paper, iris
quality is done by assessing the effect of standard deviation, contrast, area ratio, occlusion,blur, dilation and sharpness on iris images. A fusion method based on principal component analysis (PCA) is proposed to determine the quality score. CASIA, IID and UBIRIS databases are used to test the proposed algorithm. SVM was used to evaluate the performance of the
proposed quality algorithm. . The experimental results demonstrated that the proposed algorithm yields an efficiency of over 84 % and 90 % Correct Rate and Area under the Curve respectively. The use of character component to assess quality has been found to be sufficient for quality detection.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computer Science, Engineering and Information Technology. The Journal looks for significant contributions to all major fields of the Computer Science and Information Technology in theoretical and practical aspects. The aim of the Journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
The Biometric Algorithm based on Fusion of DWT Frequency Components of Enhanc...CSCJournals
The biometrics are used to authenticate a person effectively compared to conventional methods of identification. In this paper we propose the biometric algorithm based on fusion of Discrete Wavelet Transform(DWT) frequency components of enhanced iris image.The iris template is extracted from an eye image by considering horizontal pixels in an iris part.The iris template contrast is enhanced using Adaptive Histogram Equalization (AHE) and Histogram Equalization (HE).The DWT is applied on enhanced iris template.The features are formed by straight line fusion of low and high frequency coefficients of DWT.The Euclidian distance is used to compare final test features with database features. It is observed that the performance parameters are better in the case of proposed algorithm compared to existing algorithms.
Iris recognition is a method of biometric identification.
Biometric identification provides automatic recognition of an
individual based on the unique feature of physiological
characteristics or behavioral characteristic. Iris recognition is a
method of recognizing a person by analyzing the iris pattern.
This survey paper covers the different iris recognition techniques
and methods.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Iris Localization - a Biometric Approach Referring Daugman's AlgorithmEditor IJCATR
In general, there are many methods of biometric identification. But the Iris
recognition is most accurate and secure means of biometric identification. Iris has
many properties which makes it ideal biometric identification. There are many
methods used to identify the Iris location. To locate Iris many traditional methods are
used. In this we proposed such methods which can identify Iris Center(IC) as well as
localize its center. In this paper we are proposing a method which can use novel IC
localization method on the fact that the elliptical shape (ES) of Iris varies according to
the rotation of eye movement. In this paper various IC locations are generated and
stored in database. Finally the location of IC is detected by matching the ES of the Iris
of input eye image withes candidates in DB. In this paper we are comparing different
methods for Iris localization.
Similar to IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATOR (20)
MRI IMAGES THRESHOLDING FOR ALZHEIMER DETECTIONcsitconf
More than 55 illnesses are associated withMore than 55 illnesses are associated with the development of dementia and Alzheimer's disease
(AD) is the most prevalent form. Vascular dementia (VD) is the second most common form of
dementia. Current diagnosis of Alzheimer disease (Alzheimer's disease) is made by clinical,
neuropsychological, and neuroimaging assessments. Magnetic resonance imaging (MRI) can be
considered the preferred neuroimaging examination for Alzheimer disease because it allows for
accurate measurement of brain structures, especially the size of the hippocampus and related
regions. Image processing techniques has been used for processing the (MRI) image. Image
thresholding is an important concept, both in the area of objects segmentation and recognition.
It has been widely used due to the simplicity of implementation and speed of time execution.
Many thresholding techniques have been proposed in the literature. The aim of this paper is to
provide formula and their implementation to threshold images using Between-Class Variance
with a Mixture of Gamma Distributions. The algorithms will be described by given their steps,
and applications. Experimental results are presented to show good results on segmentation of
(MRI) image. the development of dementia and Alzheimer's disease
(AD) is the most prevalent form. Vascular dementia (VD) is the second most common form of
dementia. Current diagnosis of Alzheimer disease (Alzheimer's disease) is made by clinical,
neuropsychological, and neuroimaging assessments. Magnetic resonance imaging (MRI) can be
considered the preferred neuroimaging examination for Alzheimer disease because it allows for
accurate measurement of brain structures, especially the size of the hippocampus and related
regions. Image processing techniques has been used for processing the (MRI) image. Image
thresholding is an important concept, both in the area of objects segmentation and recognition.
It has been widely used due to the simplicity of implementation and speed of time execution.
Many thresholding techniques have been proposed in the literature. The aim of this paper is to
provide formula and their implementation to threshold images using Between-Class Variance
with a Mixture of Gamma Distributions. The algorithms will be described by given their steps,
and applications. Experimental results are presented to show good results on segmentation of
(MRI) image.
EDGE DETECTION IN RADAR IMAGES USING WEIBULL DISTRIBUTIONcsitconf
Radar images can reveal information about the shape of the surface terrain as well as its
physical and biophysical properties. Radar images have long been used in geological studies to
map structural features that are revealed by the shape of the landscape. Radar imagery also has
applications in vegetation and crop type mapping, landscape ecology, hydrology, and
volcanology. Image processing is using for detecting for objects in radar images. Edge
detection; which is a method of determining the discontinuities in gray level images; is a very
important initial step in Image processing. Many classical edge detectors have been developed
over time. Some of the well-known edge detection operators based on the first derivative of the
image are Roberts, Prewitt, Sobel which is traditionally implemented by convolving the image
with masks. Also Gaussian distribution has been used to build masks for the first and second
derivative. However, this distribution has limit to only symmetric shape. This paper will use to
construct the masks, the Weibull distribution which was more general than Gaussian because it
has symmetric and asymmetric shape. The constructed masks are applied to images and we
obtained good results.
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScsitconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the
global environment, and in analysing the target detection and recognition .But , segmentation
of (SAR) images is known as a very complex task, due to the existence of speckle noise.
Therefore, in this paper we present a fast SAR images segmentation based on between class
variance. Our choice for used (BCV) method, because it is one of the most effective thresholding
techniques for most real world images with regard to uniformity and shape measures. Our
experiments will be as a test to determine which technique is effective in thresholding
(extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
PLANNING BY CASE-BASED REASONING BASED ON FUZZY LOGICcsitconf
The treatment of complex systems often requires the manipulation of vague, imprecise and
uncertain information. Indeed, the human being is competent in handling of such systems in a
natural way. Instead of thinking in mathematical terms, humans describes the behavior of the
system by language proposals. In order to represent this type of information, Zadeh proposed to
model the mechanism of human thought by approximate reasoning based on linguistic
variables. He introduced the theory of fuzzy sets in 1965, which provides an interface between
language and digital worlds. In this paper, we propose a Boolean modeling of the fuzzy
reasoning that we baptized Fuzzy-BML and uses the characteristics of induction graph
classification. Fuzzy-BML is the process by which the retrieval phase of a CBR is modelled not
in the conventional form of mathematical equations, but in the form of a database with
membership functions of fuzzy rules.
SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED O...csitconf
Feature Selection (FS) has become the focus of much research on decision support systems
areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic
Algorithm (GA) wrapped Bayes Naïve (BN) based FS.
Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the
second step of the selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces 85.50%
classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then
compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and
C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are
respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is
correspondingly compared with other FS algorithms. The Obtained results have shown very
promising outcomes for the diagnosis of CAD.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
COMPUTATIONAL PERFORMANCE OF QUANTUM PHASE ESTIMATION ALGORITHMcsitconf
A quantum computation problem is discussed in this paper. Many new features that make
quantum computation superior to classical computation can be attributed to quantum coherence
effect, which depends on the phase of quantum coherent state. Quantum Fourier transform
algorithm, the most commonly used algorithm, is introduced. And one of its most important
applications, phase estimation of quantum state based on quantum Fourier transform, is
presented in details. The flow of phase estimation algorithm and the quantum circuit model are
shown. And the error of the output phase value, as well as the probability of measurement, is
analysed. The probability distribution of the measuring result of phase value is presented and
the computational efficiency is discussed.
FEEDBACK SHIFT REGISTERS AS CELLULAR AUTOMATA BOUNDARY CONDITIONScsitconf
We present a new design for random number generation. The outputs of linear feedback shift
registers (LFSRs) act as continuous inputs to the two boundaries of a one-dimensional (1-D)
Elementary Cellular Automata (ECA). The results show superior randomness features and the
output string has passed the Diehard statistical battery of tests. The design is good candidate
for parallel random number generation, has strong correlation immunity and it is inherently
amenable for VLSI implementation.
Hamming Distance and Data Compression of 1-D CAcsitconf
In this paper an application of von Neumann correction technique to the output string of some
chaotic rules of 1-D Cellular Automata that are unsuitable for cryptographic pseudo random
number generation due to their non uniform distribution of the binary elements is presented.
The one dimensional (1-D) Cellular Automata (CA) Rule space will be classified by the time run
of Hamming Distance (HD). This has the advantage of determining the rules that have short
cycle lengths and therefore deemed to be unsuitable for cryptographic pseudo random number
generation. The data collected from evolution of chaotic rules that have long cycles are
subjected to the original von Neumann density correction scheme as well as a new generalized
scheme presented in this paper and tested for statistical testing fitness using Diehard battery of
tests. Results show that significant improvement in the statistical tests are obtained when the
output of a balanced chaotic rule are mutually exclusive ORed with the output of unbalanced
chaotic rule that have undergone von Neumann density correction.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
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See how to accelerate model training and optimize model performance with active learning
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Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
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Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
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In a second workflow supporting the same use case, you’ll see:
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Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
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Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
2. 66 Computer Science & Information Technology (CS & IT)
Iris, a delicate circular diaphragm, lies between the cornea and the lens of the human eye. Since
the iris patterns do not alter significantly during a person‘s lifetime, it is considered to be one of
the most stable and precise personal identification biometric [1][3][4]. Iris recognition techniques
identifies a person by mathematically analyzing the unique random patterns that are visible within
the iris and making comparisons with an already existing database.
The basic block diagram of iris recognition system is as shown in Figure 1. The different stages in
the implementation of the system consist of segmentation, normalization, feature extraction and
matching.
Eye
image
Iris
Segmentation
Normalisation Feature
Extraction
Eye
image
Iris
Segmentation
Normalisation Feature
Extraction
Matching
Decision
DB
enrolment
Figure 1. Basic block diagram of iris recognition System
A prototype has been implemented and tested using UBIRIS database[5]. The prototype had a
recognition rate of 89% and rejection rate of 95%.
2. METHODOLOGY
The pre-processing stage requires the localization of the iris which generally involves the
detection of the edge of the iris as well as that of the pupil. Since varying levels of illumination
can result in dimensional inconsistencies between eye images due to the stretching of the iris,
normalization needs to be performed so that iris region is transformed to have fixed dimensions.
After unwrapping the normalized iris region into a rectangular region, it is encoded using Haar
wavelets to generate the iris code. In the recognition stage, Hamming distance is used for
comparison of the iris code, the most discriminating feature of the iris pattern, with the existing
iris templates.
2.1. Iris Localization
The different steps involved in the Iris Segmentation or localization is as shown in Figure 2. The
iris region approximated by a ring defined by the iris/sclera (limbic) boundary and the iris/pupil
(pupillary) boundary, needs isolation by the removal of the eyelids and eyelashes [6].
3. Computer Science & Information Technology (CS & IT) 67
Dimension
Reduction
Eye
Image
Iris
Iris
Extraction
Removal of
Eyelashes&
Eyelids
Boundary
Detection
Figure 2 Iris Segmentation Stages
2.1.1. Dimension Reduction and Iris Extraction
For the reduction of the computational complexity, the iris images are first converted into gray
scale images. For localization, the assumption that summation of the pixel values in the iris region
will be less compared to other region is used. This leads to the use of the threshold technique
based on the color of the iris. The gray level values of the iris pixels for a dark iris will be lesser
when compared to light iris. Two threshold values can be set to determine the iris region using the
histogram of the eye image. For creation of the knowledge base, eye images of both the groups
have been used so that threshold value has been computed for the extreme cases.
2.1.2. Removal of eyelids and eyelashes
Next stage is the removal of unwanted information, such as eyelashes and eyelids which can be
done using Sobel operator for detecting the edges. Sobel operator performs a 2D spatial gradient
measurement on an image and returns points where there is maximum gradient of image which
helps in edge detection as it is characterised by noticeable change in intensity. Sobel operator
consists of two 3x3 masks and which are given in equation (1) and (2) respectively.
(1)
(2)
4. 68 Computer Science & Information Technology (CS & IT)
The gradient is approximated by applying the masks to the image and computing its magnitude as
in equation (3).
(3)
To compute the gradient for the pixel of the input image I is given in equation (4).
(4)
and can be found as in equations (5) and (6). Gradient magnitude at each pixel is found
and compared with the threshold to determine, whether it’s an edge pixel or not. Sobel operator is
less sensitive to noise, due to its large convolution masks.
(5)
(6)
2.1.3. Boundary Detection
For boundary detection, the centre pixel of the eyelash removed iris image is determined and a
circular strip is extracted based on the centre co-ordinates of the pupil. For detecting the inner and
outer boundary of the iris, Integro-Differential operator [2] was used. The integro-
differential operator is defined as in equation (7).
(7)
where is the eye image, r is the radius to search for, is a Gaussian smoothing
function and s is the contour of the circle given by r,x0,y0. The operator searches for the circular
path where there is maximum change in the pixel value, by varying the radius and centre x and y
position of the circular contour. The operator is applied iteratively in order to attain precise
localization.
5. Computer Science & Information Technology (CS & IT) 69
2.2. Iris Normalization and Unwrapping
Once the iris region has been successfully segmented from an eye image, it is transformed so that
it has fixed dimensions in order to allow comparisons. Most normalization techniques are based
on transforming iris into polar coordinates, known as unwrapping process. The normalization
process will produce iris region, which have the same constant dimensions, so that two
photographs of the same iris under different conditions will have same characteristics features.
Figure 3 shows the normalised iris image.
Figure 3. Normalized iris image
In fact homogenous rubber sheet model devised by Daugman remaps each point (x,y) within the
iris region to a pair of polar co-ordinates (r,θ), where r is on the interval (0,1) and θ is angle
(0,2π). Then the normalized iris region is unwrapped into a rectangular region. Figure 4 illustrates
the mechanism of Daugman’s rubber sheet model.
r
0 1
θ
r
θ
Figure 4. Unwrapping: Daugman’s Rubber Sheet Model
The normalized remapping of iris region from cartesian co-ordinates (x,y) to non-concentric polar
representation is modeled as proposed by [7] and is given by equation (8).
(8)
where
where is the iris region image, are the original cartesian co-ordinate, are the
corresponding normalized polar co-ordinates, and are the pupil and iris
boundary respectively along the direction.
6. 70 Computer Science & Information Technology (CS & IT)
After getting the normalized polar representation of the iris region, this region is unwrapped by
choosing a constant number of points along each radial line, irrespective of how narrow or wide
the radius is at a particular angle. Thus, a 2D array is produced with vertical dimensions of radial
resolution and horizontal dimension of angular resolution.
3. FEATURE EXTRACTION
In order to provide accurate recognition of individuals, the most discriminating information
present in an iris pattern has been extracted. Only the significant features of the iris have been
encoded so that comparison between templates is done.
In the feature extraction stage, first histogram equalization is done to enhance the iris texture in
the normalized image. After this, the canny edge detector [8] is used to extract iris texture from
the normalized image. This edge detected is a 2D image and hence to reduce the dimension of
feature it is converted into a 1D energy signal. Vertical projection is the method used for the
conversion from 2D to 1D signal. Discrete wavelet transform is applied to this 1D energy signal.
As a result a set of low frequency and high frequency coefficients are obtained. Since the high
frequency coefficients do not contain any information, it is omitted and the low frequency
coefficients each of which has a dimension of 64 bytes are taken as the iris templates. The
different steps involved in the feature extraction stage are shown in Figure 5.
3.1. Histogram Equalization
This method usually increases the local contrast of many images. Through this adjustment, the
intensities can be better distributed on the histogram. This allows for areas of lower local contrast
to gain a higher contrast without affecting the global contrast. Histogram equalization
accomplishes this by effectively spreading out the most frequent intensity values. The image
obtained after histogram equalization is shown in Figure 6. The domes in the unwrapped image
are due to the eyelid occlusion.
Figure 6. Histogram Equalised Image
Histogram
equalisation
Edge
detection
Vertical
Projection
DWTNormalized
image
Iris
Features
Figure 5. Feature Extraction Stages
7. Computer Science & Information Technology (CS & IT) 71
3.2. Edge Detection
After histogram equalization iris texture will be enhanced in the normalized image. Edge
detection is performed to extract the iris texture from this enhanced image. Though several edge
detection techniques such as Sobel, Canny, Prewitt etc. are available, it was observed that Canny
edge detection technique is able to extract most of the iris texture from the enhanced image.
The Canny method [9] finds edges by looking for local maxima of the gradient of . The
gradient is calculated using the derivative of a Gaussian filter. The method uses two thresholds, to
detect strong and weak edges, and include the weak edges in the output only if they are connected
to strong edges. This method is therefore less likely than the others to be fooled by noise, and
more likely to detect true weak edges. Figure 7 shows the edge detected image using canny
operator.
Figure 7. Canny edge detected image
3.3. Vertical Projection
Vertical projection is a method used to convert the 2D signal to 1D signal. This is done to reduce
the system complexity. For vertical projection, energy of each row of the edge detected image is
calculated and is converted into a row vector. The generalized equation is shown in equation (9).
The dimension of normalized image is and is taken as . Hence, after vertical
projection its dimension is m, which is equal to 128.
(9)
3.4. Discrete Wavelet Transform
The discrete wavelet transform (DWT) decomposes the signal into mutually orthogonal set of
wavelets [10]. The DWT of signal x is calculated by passing it through a series of filters. First the
samples are passed through a low pass filter with impulse response g[n] resulting in a convolution
given in equation (10).
(10)
The signal is also decomposed simultaneously using a high pass filter h[n]. The outputs give the
detail coefficients (from the high pass filter) and approximation coefficients (from the low pass
filter) whose dimension will be 64 bytes each, since the dimension of 1D signal is 128 bytes. It is
important that two filters are related to each other and they are known as a quadrature mirror
filter. Here Haar wavelet [11] is used for wavelet transform. After wavelet transform, a set of low
frequency coefficients and high frequency coefficients each of dimension 64 bytes is obtained.
After DWT, it is observed that approximation coefficients contain information and detailed
8. 72 Computer Science & Information Technology (CS & IT)
coefficients do not have any information. Hence approximation coefficients with dimensions 64
bytes is selected as feature vector and stored in database.
4. CLASSIFICATION
In recognition stage the features of the input eye image is compared with that of the features that
is already stored in the database and if it matches, the corresponding eye image is identified
otherwise it remains unidentified. Since a bitwise comparison is necessary Hamming distance was
chosen for identification.
4.1. Hamming Distance
The Hamming distance [12] gives a measure of how many bits are the same between two bit
patterns. It is used for comparison of iris templates in the recognition stage. Hamming distance D
is given by equation (11).
(11)
where, x and y are the two bit patterns of the iris code. n indicates number of bits. Hamming
distance D gives out the number of disagreeing bits between x and y.
Ideally, the hamming distance between two iris codes generated for the same iris pattern should
be zero; however this will not happen in practice due to fact that normalization is not perfect. The
larger the hamming distances (closer to 1), the more the two patterns are different and closer this
distance to zero, the more probable the two patterns are identical. By properly choosing the
threshold upon which we make the matching decision, one can get good iris recognition results
with very low error probability.
5. RESULTS AND DISCUSSIONS
The system was tested using the UBIRIS database [5] which included 1877 images from 241
persons collected in two sessions. The images collected in the first photography session were low
noise images. On the other hand, images collected in the second session were captured under
natural luminosity factor, thus allowing reflections, different contrast levels, and luminosity and
focus problems thus making it a good model for realistic situations.
Ten sets of eye images from UBIRIS database was taken for identification. Each set consists of
three eye images of a person taken at different time. From each set a single eye image was
randomly selected and its features were stored in the database. Therefore a total of 18 images
were used for simulation. These images are called registered images since its feature is stored in
the knowledge base. The main challenge in the identification is to identify the other two images in
each set whose features are not stored. 6 images whose features are not stored in the database are
also used to test the algorithm. These images are called as unregistered images. An efficient
algorithm should identify all registered images and reject all unregistered images. Performance of
iris acceptance algorithm is validated using four parameters -False Reject (FR), False
Accept(FA), Correct Reject(CR) and Correct Accept(CA). FR is obviously the case where we
judge a pattern as not the target one while it is. FA is when the pattern is considered as the target
one while it is not. CR is when the pattern is correctly judged as being not the target one. Finally,
CA is when the pattern is correctly considered to be the targeted one. These outcomes are
9. Computer Science & Information Technology (CS & IT) 73
illustrated in Figure 8. It was found that an optimum result is obtained at hamming distance
threshold of 0.4.
0
1
2
3
4
5
6
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
ProbabilityDensity
Hamming Distance
2
4
3
1. Correct accept rate
2. False accept rate
3. Flase rejectrate
4. Correct rejectrate
threshold
Authentics Imposters
1
Figure 8. Decision making in iris biometric system
Figure 9. Detection of iris edge using various operators
Using MATLAB, a comparison study between different classical operators, Canny, Sobel,
Prewitt, Roberts, log and zero cross was also done. The operators were applied to the enhanced
normalized image. The results, presented in Figure 9, show the performance of each of the
operators. It was found that the Canny operator outperforms the others; in fact it was the only
operator which was able to extract most of the iris texture.
6. CONCLUSIONS
Iris images were segmented and projected into 1D signal by the vertical projection and the 1D
signal features were extracted by the Haar wavelet transform. The complexity of feature
extraction method for iris recognition was low and achieved thus a considerable computational
reduction while keeping good performance.
A low dimensional feature extraction algorithm was developed and tested with 24 eye images
from the database by varying the parameters such as dimension of feature vector and hamming
distance threshold. It was found that an optimum result is obtained at hamming distance threshold
of 0.4 and feature vector dimension of 64bytes. It was also observed that canny operator was best
(a) Canny
(c)Prewitt
(e) Log
(d) Robert
(b) Sobel
(f)Zero Cross
10. 74 Computer Science & Information Technology (CS & IT)
suited to extract most of the edges to generate the iris code for comparison. Recognition rate of
89% and rejection rate of 95% was achieved.
REFERENCES
[1] Jain A.K, Ross A. and Prabhakar S.(2004) “An Introduction to Biometric Recognition”, IEEE
Transactions on Circuits and Systems for Video Technology, Special Issue on Image- and Video-
Based Biometrics, Vol.14, No.1, pp.4–20.
[2] Daugman J.(2004) “How iris recognition works”, IEEE Transactions on Circuits and Systems for
Video Technology, Vol.14, No.1, pp.21-30.
[3] Jain A.K, Ross A, Pankanti S,(2006) “Biometrics: A tool for information security”, IEEE Transaction
on Information Forensics and Security, Vol.1, No.2, pp.125-143.
[4] Ross A, Nandakumar K & Jain A.K,(2006) “Handbook of Multibiometrics”, Springer.
[5] Proenc H, and Alexandre L,(2004) “UBIRIS: Iris Image Database”, Available: http://iris.di.ubi.pt.
[6] Zhang D.(2003) “Detecting eyelash and reflection for accurate iris segmentation”, International
journal of Pattern Recognition and Artificial Intelligence, Vol. 1, No.6, pp.1025-1034..
[7] Jaroslav B, “Circle detection using Hough Transform,” Technical Report, University of Bristol, U.K.
Available at: http://linux.fjfi.cvut.cz/~pinus/bristol/ imageproc/ hw1/report.pdf
[8] Kovesi P, “MATLAB Function for Computer Vision and Image Analysis”, Available at:
http://www.cs. uwa. edu.au/ ~pk/Research/ MatLabFns/ index.html.
[9] Canny J,(1986) “A computational approach to edge detection”, IEEE Transaction on Pattern Analysis
and Machine Intelligence, Vol. 8, pp. 679-698.
[10] Boles W, and Boashash B,(1998), “A Human Identification Technique using Images of the Iris and
Wavelet Transform”, IEEE Trans. Signal Processing, Vol. 46, No. 4, pp. 1185-1188.
[11] Lim S, Lee K., Byeon O, and Kim T,(2001) “Efficient iris recognition through improvement of
feature vector and classifier”, ETRI Journal, Vol. 23, No. 2, pp. 61-70.
[12] Daugman J,(1993) “High confidence Visual Recognition of Person by a test of statistical
Independence”, IEEE transactions on pattern analysis and machine intelligence, Vol.15 No.11, pp.
1148-1161.
Authors
Binsu C. Kovoor is working as Assistant Professor in Information Technology, Cochin
University of Science and Technology 2000 onwards. Her areas of interest include
biometric security system, pattern recognition, database systems, data mining, data
structures, streaming audio and video signals etc. She is a Life member of ISTE and IE.
Dr. Supriya M. H joined as a faculty in the Department of Electronics, Cochin
University of Science & Technology in 1999. Her fields of interests are Target
Identification, Signal Processing, Bioinfomatics, Steganography and Computer
Technology. She has presented papers in several International Conferences in Europe,
USA, and France. She is actively involved in Research and Development activities in
Ocean Electronics and related areas and has a patent and about 87 research publications
to her credit. She is a Life member of IETE and ISTE.
Dr. K. Poulose Jacob, Professor of Computer Science at Cochin University of Science
and Technology since 1994, is currently Director of the School of Computer Science
Studies. His research interests are in Information Systems Engineering, I ntelligent
Architectures and Networks.He has more than 90 research publications to his credit. He
has presented papers in several International Conferences in Europe, USA, UK,
Australia and other countries. Dr. K. Poulose Jacob is a Professional member of the
ACM (Association for Computing Machinery) and a Life Member of the Computer
Society of India.