This document provides an overview of iris recognition technology. It discusses what the iris is, why it is useful for biometric identification, the history and applications of iris recognition. It then describes the main steps in an iris recognition system: image acquisition, segmentation, normalization, feature encoding, matching. It discusses some common feature encoding and matching methods. In conclusion, iris recognition is considered the most accurate biometric technology due to the iris's complex patterns and stability over time.
IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATORcsitconf
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 BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATORcsitconf
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.
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 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.
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
Biometrics Iris Scanning: A Literature ReviewOlivia Moran
The interest in Biometrics from both governments and industry has lead to the emergence of multiple Biometric technologies all with their own strengths and flaws. One currently at the forefront of Biometrics is iris scanning.
The process involved in the identification and verification of people using iris scanning is examined in this paper. The advantages and disadvantages associated with the utilisation of such a technology are also explored. A number of legal and ethical issues are highlighted. Iris scanning is looked at in comparison to other forms of Biometric technologies. Future work in the area of Biometrics is also considered in light of current developments.
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 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.
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
Biometrics Iris Scanning: A Literature ReviewOlivia Moran
The interest in Biometrics from both governments and industry has lead to the emergence of multiple Biometric technologies all with their own strengths and flaws. One currently at the forefront of Biometrics is iris scanning.
The process involved in the identification and verification of people using iris scanning is examined in this paper. The advantages and disadvantages associated with the utilisation of such a technology are also explored. A number of legal and ethical issues are highlighted. Iris scanning is looked at in comparison to other forms of Biometric technologies. Future work in the area of Biometrics is also considered in light of current developments.
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.
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.
Pupil Detection Based on Color Difference and Circular Hough Transform IJECEIAES
Human pupil eye detection is a significant stage in iris segmentation which is representing one of the most important steps in iris recognition. In this paper, we present a new method of highly accurate pupil detection. This method is consisting of many steps to detect the boundary of the pupil. First, the read eye image (R, G, B), then determine the work area which is consist of many steps to detect the boundary of the pupil. The determination of the work area contains many circles which are larger than pupil region. The work area is necessary to determine pupil region and neighborhood regions afterward the difference in color and intensity between pupil region and surrounding area is utilized, where the pupil region has color and intensity less than surrounding area. After the process of detecting pupil region many steps on the resulting image is applied in order to concentrate the pupil region and delete the others regions by using many methods such as dilation, erosion, canny filter, circle hough transforms to detect pupil region as well as apply optimization to choose the best circle that represents the pupil area. The proposed method is applied for images from palacky university, it achieves to 100 % accuracy
IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATORcscpconf
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.
A New Approach of Iris Detection and RecognitionIJECEIAES
This paper proposes an IRIS recognition and detection model for measuring the e-security. This proposed model consists of the following blocks: segmentation and normalization, feature encoding and feature extraction, and classification. In first phase, histogram equalization and canny edge detection is used for object detection. And then, Hough Transformation is utilized for detecting the center of the pupil of an IRIS. In second phase, Daugmen’s Rubber Sheet model and Log Gabor filter is used for normalization and encoding and as a feature extraction method GNS (Global Neighborhood Structure) map is used, finally extracted feature of GNS is feed to the SVM (Support Vector Machine) for training and testing. For our tested dataset, experimental results demonstrate 92% accuracy in real portion and 86% accuracy in imaginary portion for both eyes. In addition, our proposed model outperforms than other two conventional methods exhibiting higher accuracy.
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.
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%.
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.
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.
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.
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
AN IMPROVED IRIS RECOGNITION SYSTEM BASED ON 2-D DCT AND HAMMING DISTANCE TEC...IJEEE
This paper proposes a new iris recognition system that implements Integro-Differential, Daugman Rubber Sheet Model, 2-D DCT, Hamming Distance to exact features from the iris and matching it with the sorted database.All these image-processing algorithms have been validated on noised real iris images & UBIRIS database
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
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2. Introduction
What the iris?
Why iris?
History of iris Recognition
Applications
Methods of iris recognition system
Image Acquisition
Segmentation
Normalization
Iris Feature Encoding
Iris code matching
Applications
Disadvantages
Conclusion
References
3. It is considered to be the most accurate biometric
technology available today.
Iris recognition is a method of
biometric identification and
authentication that use pattern-
recognition techniques based on
high resolution images of the
irises of an individual's eyes .
5. The iris is a thin circular diaphragm, which lies
between the cornea and the lens of the human eye.
The iris is perforated close to its centre by a circular
aperture known as the pupil.
The function of the iris is to control the amount of light
entering through the pupil.
The average diameter of the iris is 12 mm, and the
pupil size can vary from 10% to 80% of the iris
diameter [2].
6. The iris consists of a number of layers, the lowest is
the epithelium layer, which contains dense
pigmentation cells.The stromal layer lies above the
epithelium layer, and contains blood vessels,
pigment cells and the two iris muscles.
7. The density of stromal pigmentation determines the
colour of the iris.
The externally visible surface of the
multi-layered iris contains two zones,
which often differ in colour An outer
ciliary zone and an inner pupillary zone,
and these two zones are divided by the
collarette – which appears as a zigzag
pattern[3].
8. Externally visible highly protected internal
organ.
Unique patterns.
Not genetically connected unlike eye color.
Stable with age.
Impossible to alter surgically.
Living Password, Can not be forgotten or copied.
Works on blind person.
User needs not to touch appliances.
Accurate , faster , and supports large data base.
11. 1997-1999
1987
1987
1980
The concept of Iris Recognition was first proposed by
Dr. Frank Burch in 1939.
It was first implemented in 1990
when Dr. John Daugman created the
algorithms for it.
These algorithms employ methods
of pattern recognition and some
mathematical calculations for iris
recognition.
12. . ATMs
.Computer login:The iris as a living
password.
· National Border Controls
· Driving licenses and other personal
certificates.
· benefits authentication.
·birth certificates, tracking missing.
· Credit-card authentication.
· Anti-terrorism (e.g.:— suspect
Screening at airports)
· Secure financial transaction (e-
commerce, banking).
· Internet security, control of access to
privileged information.
13. In identifying one’s iris, there are 2 methods for its
recognition and are:
1. Active
2. Passive
The active Iris system requires that a user be anywhere
from six to fourteen inches away from the camera.
The passive system allows the user to be anywhere
from one to three feet away from the camera that
locates the focus on the iris.
15. The first step, image acquisition
deals with capturing sequence of iris
images from the subject using
cameras and sensors with High
resolution and good sharpness.
These images should clearly show
the entire eye especially iris and
pupil part, and then some
preprocessing operation may be
applied to enhance the quality of
image e.g. histogram equalization,
filtering noise removal etc.
(CASIA) eye image database
16. The first stage of iris segmentation
to isolate the actual iris region in a
digital eye image.
The iris region, can be
approximated by two circles, one
for the iris/sclera boundary and
another, interior to the first, for
the iris/pupil boundary.
17. the derivatives in the horizontal direction for detecting
the eyelids, and in the vertical direction for detecting the
outer circular boundary of the iris .
Taking only the vertical gradients for locating the iris
boundary will reduce influence of the eyelids when
performing circular Hough transform.
18. The circular Hough transform can be employed to deduce the
radius and centre coordinates of the pupil and iris regions:
Firstly, an edge map is generated by calculating the first
derivatives of intensity values in an eye image and then
thresholding the result.
From the edge map, votes are cast in Hough space for the
parameters of circles passing through each edge point,These
parameters are the centre coordinates xc and yc, and the radius r,
which are able to define any circle according to the equation :
A maximum point in the Hough space will correspond to the
radius and centre coordinates of the circle best defined by the
edge points.
19. eyelashes are treated as belonging to two types :
1 -separable eyelashes:
which are isolated in the image .
2-multiple eyelashes:
which are bunched together and overlap in the eye image.
Eyelids and Eyelashes are the main noise factor in the iris image.
These noise factors can affect the accuracy of the iris recognition system.
After applying circular Hough transform to iris, we are applying linear Hough
transform and we get line detected noise region in the iris image.
We have to remove these detected eyelids and eyelashes from the iris image
Thresolding is used for the removal of eyelashes.Then, the noise free iris
image can be available for future use.
21. Process of finding the iris in an image
a. Iris and pupil localization: Pupil and Iris are considered as
two circles using Circular HoughTransform .
b. Eye lid detection and Eye lash noise removal using linear Hough
Transform method.
22. Various Normalisation methods :
1- Daugman’s Rubber sheet Model by
Daugman [2]
2- Image Registration modlyed byWildes et al
.[9]
3-Virtual Circles by Boles [14] .
23. Once the iris segmented ,the next stage transform the iris
region so that it has fixed dimensions in order to allow
comparisons.
Since variations in the eye like pupil dilation and the
inconsistence iris normalization is needed.
Pupil dilation inconsistence iris
Normalization process involves unwrapping the iris and
converting it in to its polar equivalent .
24. It is done using Daugman’s Rubber sheet model .
The centre of the pupil was considered as the reference
point, and radial vectors pass through the iris region .
A number of data points are selected along each radial line is
defined as the radial resolution.The number of radial lines
going around the iris region is defined as the angular
resolution.
25. where displacement of the center of the pupil relative to the center of the iris is given by 𝑜 𝑥, 𝑜 𝑦 .
r’ is the distance between the edge of the pupil and edge of the iris at an angle, θ around the region, and rIis the
radius of the iris.
The remapping formula first gives the radius of the iris region as a function of the angle θ.
26. Normalisation produces a 2D array with horizontal
dimensions of angular resolution and vertical dimensions of
radial resolution.
Rubber sheet model does not compensate for rotational
inconsistencies
27. Various feature encoding methods :
1-Gabor Filters employed by Daugman in [2] andTuama.[6]
2- Log-Gabor Filters employed by D. Field.[15]
.
3- HaarWavelet employed by Lim et al.. [16]
4- Zero –crossing of the 1D wavelet employed by Boles and
Boashash .[14]
5- Laplacian of gaussian filters employed byWildes et al[9]
28. Feature Encoding : creating a template containing only the
most discriminating features of the iris .
Extracted the features of the normalized iris by filtering the
normalized iris region . [6]
a Gabor filter is a sine ( or cosine) wave modulated by a
Gaussian . it is applied on the entire image at once and
unique features are extracted from the image
Feature encoding was implemented by convolving the
normalized iris with 1D Gabor wavelets.
29. The Daugman system makes use of polar coordinates for
normalisation, therefore in polar form the filters are given as :
(r0, θ0) specify the centre frequency of the filter. (α,β) specify the effective width and length.
The angular direction is taken rather than the radial one ,
since maximum independence occurs in the angular direction
.
30. Daugman demodulates the output of the Gabor filters in
order to compress the data this is done by quantising the
phase information in to four levels , for each possible
quadrant in the complex plane . [7]
The demodulation and phase Quantisation process can be
represented as
where h{Re, Im} can be regarded as a complex valued bit whose real and imaginary components are dependent
on the sign of the 2D integral, and I( ρ,θ ) is the raw iris image in a dimensionless polar coordinate system.
31. Using real and imaginary values, the phase information is
extracted and encoded in a binary pattern .
The total number of bits in the template will be the angular
resolution times the radial resolution , times 2, times number
of filters used .
The number of filters,their centre frequencies and parameters
of the modulating Gaussian function must be detecting
according to the used data base .
32.
33. Various feature matching methods :
1- Hamming distance employed by Daugman [2]
2-Weighted Euclidean Distance employed by Zhu et al[17] .
3- Normalised correlation employed byWildes [9] .
34. The Hamming Distance was chosen as a matching metric ,
which gave a measure of how many bits disagreed between
two templates .
When the hamming distance of two templates is calculated ,
one template is shifted left and right bit-wise and a number
of hamming distance values are calculated from successive
shifts , in order to account for rotational inconsistencies .
35. The actual number of shifts required to normalise rotational
inconsistencies will be determined by the maximum angle
difference between two images of the same eye .
One shift is defined as one shift to the left , followed by one
shift to the right .
This method is suggested by Daugman . [7]
36.
37. The Chines Academy of Sciences – Institute of Automation
(CASIA) eye image database contains 756 greyscale eye
images with 108 unique eyes or class are taken from two
sessions .[8]
38. Threshold FAR (%) FRR (%)
0.20 0.000 99.047
0.25 0.000 82.787
0.30 0.000 37.880
0.35 0.000 5.181
0.40 0.005 0.238
0.45 7.599 0.000
0.50 99.499 0.000
Table 1 – False accept and false reject rates for the ‘CASIA-a’ data set with
different separation points using the optimum parameters.
39. Accuracy changes with user’s height ,illumination , Image
quality etc.
Person needs to be still, difficult to scan if not co-operated.
Risk of fake Iris lenses.
Alcohol consumption causes deformation in Iris pattern
Expensive .
40. Highly accurate but easy
Fast
Needs some developments
Experiments are going on
Will become day to day technology very soon
41. [1] · http://www.cl.cam.ac.uk
[2] J. Daugman. How iris recognition works. Proceedings of 2002 International
Conference on Image Processing,Vol. 1, 2002.
[3]E.Wolff. Anatomy of the Eye and Orbit. 7th edition. H. K. Lewis & Co. LTD, 1976.
[4] L.Flom and A. Safir : Iris Recognition System .U.S. atent No.4641394(1987).
[5]T. Chuan Chen K . Liang Chung : An Efficient Randomized Algorithm for
Detecting Circles.
Computer vision and Image UnderstandingVol.83(2001) 172-191.
[6] Amel saeedTuama “ It is Image Segmentation and RecognitionTechnology”
vol-3 No.2 April 2012 .
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