Iris Recognition
http://www.cl.cam.ac.uk/~jgd1000/irisrecog.pdf
Papers
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Black books
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PowerPoint presentation
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Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of the irides of an individual's eyes, whose complex random patterns are unique and can be seen from some distance.
Not to be confused with other, less prevalent, ocular-based technologies, retina scanning and eye printing, iris recognition uses camera technology with subtle infrared illumination to acquire images of the detail-rich, intricate structures of the iris externally visible at the front of the eye. Digital templates encoded from these patterns by mathematical and statistical algorithms allow the identification of an individual or someone pretending to be that individual.Databases of enrolled templates are searched by matcher engines at speeds measured in the millions of templates per second per (single-core) CPU, and with remarkably low false match rates.
Many millions of persons in several countries around the world have been enrolled in iris recognition systems, for convenience purposes such as passport-free automated border-crossings, and some national ID systems based on this technology are being deployed. A key advantage of iris recognition, besides its speed of matching and its extreme resistance to false matches, is the stability of the iris as an internal, protected, yet externally visible organ of the eye.
Not to be confused with other, less prevalent, ocular-based technologies, retina scanning and eye printing, iris recognition uses camera technology with subtle infrared illumination to acquire images of the detail-rich, intricate structures of the iris externally visible at the front of the eye. Digital templates encoded from these patterns by mathematical and statistical algorithms allow the identification of an individual or someone pretending to be that individual.Databases of enrolled templates are searched by matcher engines at speeds measured in the millions of templates per second per (single-core) CPU, and with remarkably low false match rates.
Many millions of persons in several countries around the world have been enrolled in iris recognition systems, for convenience purposes such as passport-free automated border-crossings, and some national ID systems based on this technology are being deployed. A key advantage of iris recognition, besides its speed of matching and its extreme resistance to false matches, is the stability of the iris as an internal, protected, yet externally visible organ of the eye.
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1402iris.pdf | |
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Literature Survey
Although John Daugman developed and patented the first actual algorithms to perform iris recognition, published the first papers about it and gave the first live demonstrations, the concept behind this invention has a much longer history and today it benefits from many other active scientific contributors. In a 1953 clinical textbook, F.H. Adler wrote: "In fact, the markings of the iris are so distinctive that it has been proposed to use photographs as a means of identification, instead of fingerprints." Adler referred to comments by the British ophthalmologist J.H. Doggart, who in 1949 had written that: "Just as every human being has different fingerprints, so does the minute architecture of the iris exhibit variations in every subject examined. [Its features] represent a series of variable factors whose conceivable permutations and combinations are almost infinite." Later in the 1980s, two American ophthalmologists, L. Flom and A. Safir managed to patent Adler's and Doggart's conjecture that the iris could serve as a human identifier, but they had no actual algorithm or implementation to perform it and so their patent remained conjecture. The roots of this conjecture stretch back even further: in 1892 the Frenchman A. Bertillon had documented nuances in "Tableau de l'iris humain". Divination of all sorts of things based on iris patterns goes back to ancient Egypt, to Chaldea in Babylonia, and to ancient Greece, as documented in stone inscriptions, painted ceramic artefacts, and the writings of Hippocrates. (Iris divination persists today, as "iridology.")
The core theoretical idea in Daugman's algorithms is that the failure of a test of statistical independence can be a very strong basis for pattern recognition, if there is sufficiently high entropy (enough degrees-of-freedom of random variation) among samples from different classes. In 1994 he patented this basis for iris recognition and its underlying Computer Vision algorithms for image processing, feature extraction, and matching, and published them in a paper. These algorithms became widely licensed through a series of companies: IriScan (a start-up founded by Flom, Safir, and Daugman), Iridian, Sarnoff, Sensar, LG-Iris, Panasonic, Oki, BI2, IrisGuard, Unisys, Sagem, Enschede, Securimetrics and L-1, now owned by French company Morpho.
With various improvements over the years, these algorithms remain today the basis of all significant public deployments of iris recognition, and they are consistently top performers in NIST tests (implementations submitted by L-1, MorphoTrust and Morpho, for whom Daugman serves as Chief Scientist for Iris Recognition). But research on many aspects of this technology and on alternative methods has exploded, and today there is a rapidly growing academic literature on optics, photonics, sensors, biology, genetics, ergonomics, interfaces, decision theory, coding, compression, protocol, security, mathematical and hardware aspects of this technology. Most flagship deployments of these algorithms have been at airports, in lieu of passport presentation, and for security screening using watch-lists. In the early years of this century, major deployments began at Amsterdam's Schiphol Airport and at ten UK airport terminals allowing frequent travellers to present their iris instead of their passport, in a programme called IRIS: Iris Recognition Immigration System. Similar systems exist along the US / Canadian border, and many others. In the United Arab Emirates, all 32 air, land, and seaports deploy these algorithms to screen all persons entering the UAE requiring a visa. Because a large watch-list compiled among GCC States is exhaustively searched each time, the number of iris cross-comparison has climbed to 62 trillion in 10 years. But by far the most breathtaking deployment began operation in 2011 in India, whose Government is enrolling the iris patterns (and other biometrics) of all 1.2 billion citizens for the Aadhaar scheme for entitlements distribution, run by the Universal Identification Authority of India (UIDAI). This programme enrolls about one million persons every day, across 36,000 stations operated by 83 agencies. By late 2013, the number of persons enrolled exceeded 530 million. Its purpose is to issue each citizen a biometrically provable unique entitlement number (Aadhaar) by which benefits may be claimed, and social inclusion enhanced; thus the slogan of UIDAI is: "To give the poor an identity."
All publicly deployed iris recognition systems acquire images of an iris in the near infrared wavelength band (NIR: 700–900 nm) of the electromagnetic spectrum. The majority of persons worldwide have "dark brown eyes", the dominant phenotype of the human population, revealing less visible texture in the VW band but appearing richly structured, like the cratered surface of the moon, in the NIR band. (Some examples are shown here.) Using the NIR spectrum also enables the blocking of corneal specular reflections from a bright ambient environment, by allowing only those NIR wavelengths from the narrow-band illuminator back into the iris camera.
Iris melanin, also known as chromophore, mainly consists of two distinct heterogeneous macromolecules, called eumelanin (brown–black) and pheomelanin (yellow–reddish),whose absorbance at longer wavelengths in the NIR spectrum is negligible. At shorter wavelengths within the VW spectrum, however, these chromophores are excited and can yield rich patterns. Hosseini, et al.provide a comparison between these two imaging modalities. An alternative feature extraction method to encode VW iris images was also introduced, which may offer an alternative approach for multi-modal biometric systems.
University of Tehran IRIS (UTIRIS) image repository provides the first hybrid iris databank registered in two distinct sessions: Visible Wavelength (VW) and Near InfraRed (NIR) during 24–27 June 2007.
The core theoretical idea in Daugman's algorithms is that the failure of a test of statistical independence can be a very strong basis for pattern recognition, if there is sufficiently high entropy (enough degrees-of-freedom of random variation) among samples from different classes. In 1994 he patented this basis for iris recognition and its underlying Computer Vision algorithms for image processing, feature extraction, and matching, and published them in a paper. These algorithms became widely licensed through a series of companies: IriScan (a start-up founded by Flom, Safir, and Daugman), Iridian, Sarnoff, Sensar, LG-Iris, Panasonic, Oki, BI2, IrisGuard, Unisys, Sagem, Enschede, Securimetrics and L-1, now owned by French company Morpho.
With various improvements over the years, these algorithms remain today the basis of all significant public deployments of iris recognition, and they are consistently top performers in NIST tests (implementations submitted by L-1, MorphoTrust and Morpho, for whom Daugman serves as Chief Scientist for Iris Recognition). But research on many aspects of this technology and on alternative methods has exploded, and today there is a rapidly growing academic literature on optics, photonics, sensors, biology, genetics, ergonomics, interfaces, decision theory, coding, compression, protocol, security, mathematical and hardware aspects of this technology. Most flagship deployments of these algorithms have been at airports, in lieu of passport presentation, and for security screening using watch-lists. In the early years of this century, major deployments began at Amsterdam's Schiphol Airport and at ten UK airport terminals allowing frequent travellers to present their iris instead of their passport, in a programme called IRIS: Iris Recognition Immigration System. Similar systems exist along the US / Canadian border, and many others. In the United Arab Emirates, all 32 air, land, and seaports deploy these algorithms to screen all persons entering the UAE requiring a visa. Because a large watch-list compiled among GCC States is exhaustively searched each time, the number of iris cross-comparison has climbed to 62 trillion in 10 years. But by far the most breathtaking deployment began operation in 2011 in India, whose Government is enrolling the iris patterns (and other biometrics) of all 1.2 billion citizens for the Aadhaar scheme for entitlements distribution, run by the Universal Identification Authority of India (UIDAI). This programme enrolls about one million persons every day, across 36,000 stations operated by 83 agencies. By late 2013, the number of persons enrolled exceeded 530 million. Its purpose is to issue each citizen a biometrically provable unique entitlement number (Aadhaar) by which benefits may be claimed, and social inclusion enhanced; thus the slogan of UIDAI is: "To give the poor an identity."
All publicly deployed iris recognition systems acquire images of an iris in the near infrared wavelength band (NIR: 700–900 nm) of the electromagnetic spectrum. The majority of persons worldwide have "dark brown eyes", the dominant phenotype of the human population, revealing less visible texture in the VW band but appearing richly structured, like the cratered surface of the moon, in the NIR band. (Some examples are shown here.) Using the NIR spectrum also enables the blocking of corneal specular reflections from a bright ambient environment, by allowing only those NIR wavelengths from the narrow-band illuminator back into the iris camera.
Iris melanin, also known as chromophore, mainly consists of two distinct heterogeneous macromolecules, called eumelanin (brown–black) and pheomelanin (yellow–reddish),whose absorbance at longer wavelengths in the NIR spectrum is negligible. At shorter wavelengths within the VW spectrum, however, these chromophores are excited and can yield rich patterns. Hosseini, et al.provide a comparison between these two imaging modalities. An alternative feature extraction method to encode VW iris images was also introduced, which may offer an alternative approach for multi-modal biometric systems.
University of Tehran IRIS (UTIRIS) image repository provides the first hybrid iris databank registered in two distinct sessions: Visible Wavelength (VW) and Near InfraRed (NIR) during 24–27 June 2007.
Code 1
I =imread('C:\Users\Ninad\Pictures\1.jpg');
imshow(I);
code 2
A=[1 1 1; 1 0 1; 1 1 1]
imshow(A)
I =imread('C:\Users\Ninad\Pictures\1.jpg');
imshow(I);
code 2
A=[1 1 1; 1 0 1; 1 1 1]
imshow(A)
close all
clear all
clc
%imaqhwinfo;
%dev_info = imaqhwinfo('winvideo',1)
vid=videoinput('winvideo',1, 'YUY2_640x480');
%preview(vid);
im=getsnapshot(vid);
figure(),imshow(im);
%% conversion of data format optional
im=ycbcr2rgb(im);
figure(),imshow(im);
%% backup of image
I=im;
%%convert image to gray scale for edge detection
I = rgb2gray(I);
figure(),imshow(I);
%% Edge detection
I=edge(I,'canny');
figure(),imshow(I);
clear all
clc
%imaqhwinfo;
%dev_info = imaqhwinfo('winvideo',1)
vid=videoinput('winvideo',1, 'YUY2_640x480');
%preview(vid);
im=getsnapshot(vid);
figure(),imshow(im);
%% conversion of data format optional
im=ycbcr2rgb(im);
figure(),imshow(im);
%% backup of image
I=im;
%%convert image to gray scale for edge detection
I = rgb2gray(I);
figure(),imshow(I);
%% Edge detection
I=edge(I,'canny');
figure(),imshow(I);
In this paper, a combination of fast and cooperative modular neural nets to enhance the performance of the detection process is introduced. I have applied such approach successfully to detect human faces in cluttered scenes, [11]. Here, this technique is used to identify human irises automatically in a given image. Neural nets are used to test whether a window of 20×20 pixels contains an iris or not. The major difficulty in the learning process comes from the large database required for iris/non-iris images. A simple design for cooperative modular neural nets is presented to solve this problem by dividing these data into three groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. Simulation results for the proposed algorithm show a good performance. Moreover, a powerful system for personal identification using iris detection is presented. Furthermore, faster iris detection is obtained through image decomposition into many sub-images and applying cross correlation in the frequency domain between each sub-image and the weights of the hidden layer.
close all
clear all
clc
%imaqhwinfo;
%dev_info = imaqhwinfo('winvideo',1)
vid=videoinput('winvideo',1, 'YUY2_640x480');
%preview(vid);
im=getsnapshot(vid);
figure(),imshow(im);
%% conversion of data format optional
im=ycbcr2rgb(im);
figure(),imshow(im);
%% backup of image
I=im;
IrisDetector = vision.CascadeObjectDetector('LeftEye');
BB = step(IrisDetector,I);
%% display the image
figure,
imshow(I);
hold on
for i = 1:size(BB,1)
if(i==1)
rectangle('Position',BB(i,:),'LineWidth',0.1,'LineStyle','-','EdgeColor','b');
display(i);
end
end
%%convert image to gray scale for edge detection
I = rgb2gray(I);
figure(),imshow(I);
%% Edge detection
I=edge(I,'canny');
figure(),imshow(I);
clear all
clc
%imaqhwinfo;
%dev_info = imaqhwinfo('winvideo',1)
vid=videoinput('winvideo',1, 'YUY2_640x480');
%preview(vid);
im=getsnapshot(vid);
figure(),imshow(im);
%% conversion of data format optional
im=ycbcr2rgb(im);
figure(),imshow(im);
%% backup of image
I=im;
IrisDetector = vision.CascadeObjectDetector('LeftEye');
BB = step(IrisDetector,I);
%% display the image
figure,
imshow(I);
hold on
for i = 1:size(BB,1)
if(i==1)
rectangle('Position',BB(i,:),'LineWidth',0.1,'LineStyle','-','EdgeColor','b');
display(i);
end
end
%%convert image to gray scale for edge detection
I = rgb2gray(I);
figure(),imshow(I);
%% Edge detection
I=edge(I,'canny');
figure(),imshow(I);
Iris detection system link
iris detection system link 2
link 3
what is the genetic algorithm:
The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals at random from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution. You can apply the genetic algorithm to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, non-differentiated, stochastic, or highly nonlinear. The genetic algorithm can address problems of mixed integer programming, where some components are restricted to be integer-valued.
You can apply the genetic algorithm to solve problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, non-differentiation, stochastic, or highly nonlinear.
The genetic algorithm differs from a classical, derivative-based, optimization algorithm in two main ways, as summarized in the following:
Classical algorithm:
Generates a single point at each iteration. The sequence of points approaches an optimal solution.
Selects the next point in the sequence by a deterministic computation.
Genetic algorithm:
Generates a population of points at each iteration. The best point in the population approaches an optimal solution.
Selects the next population by computation which uses random number generators.
The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals at random from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution. You can apply the genetic algorithm to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, non-differentiated, stochastic, or highly nonlinear. The genetic algorithm can address problems of mixed integer programming, where some components are restricted to be integer-valued.
You can apply the genetic algorithm to solve problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, non-differentiation, stochastic, or highly nonlinear.
The genetic algorithm differs from a classical, derivative-based, optimization algorithm in two main ways, as summarized in the following:
Classical algorithm:
Generates a single point at each iteration. The sequence of points approaches an optimal solution.
Selects the next point in the sequence by a deterministic computation.
Genetic algorithm:
Generates a population of points at each iteration. The best point in the population approaches an optimal solution.
Selects the next population by computation which uses random number generators.