ppt on iris recognition 1

PERSONAL IDENTIFICATION BASED ON

IRIS PATTERN

ByRoll No: 10224002M.Tech (Computer

Tech.)NIT

Under the guidance ofAssistant professorDept. of Electrical

EngineeringNIT

PERSONAL IDENTIFICATION BASED ON IRIS PATTERN

CONTENTS

1.INTRODUCTION OF IRIS RECOGNITION

• What is Iris Recognition

• Human Iris• Operating Principle• Advantages• Disadvantages• History

2. STATE OF THE ART

CONTENTS (CNTD….)

3. TECHNICAL ISSUES Image Acquisition Segmentation Normalization Feature Encoding And Matching Iris Image Database

4 PERFORMANCE METRICS FOR IRIS RECOGNITION

5. APPLICATIONS OF IRIS RECOGNITION

6. REFERENCES

1. INTRODUCTION OF IRIS RECOGNITION1.1 What Is Iris Recognition

Iris recognition is a method of biometric authentication that uses pattern-recognition techniques based on high-resolution images of the iris of an individual's eyes.

A Iris recognition system provides Personal identification of an individual based on a unique feature or characteristic possessed by the human Iris.

The physiological complexity of the organ results in the random patterns in iris, which are statistically unique and suitable for biometric measurements.

INTRODUCTION OF IRIS RECOGNITION(CNTD….)

1.2 Human Iris The iris is a thin circular diaphragm, which lies between the

cornea and the lens of the human eye. front-on view of the iris is shown in Figure 1.1.

Figure 1.1 – A front-on view of the human eye.

1.2 HUMAN IRIS(CNTD….) 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, and this is done by the sphincter and the dilator muscles, which adjust the size of 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 .

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. The density of stromal pigmentation determines the colour of the iris.

1.2 HUMAN IRIS(CNTD….) Formation of the iris begins during the third month of

embryonic life [3]. The unique pattern on the surface of the iris is formed during the first year of life, and pigmentation of the stroma takes place for the first few years. Formation of the unique patterns of the iris is random and not related to any genetic factors [4].

The only characteristic that is dependent on genetics is the pigmentation of the iris, which determines its colour. Due to the epigenetic nature of iris patterns, the two eyes of an individual contain completely independent iris patterns, and identical twins possess uncorrelated iris patterns[3].


1.3 Operating Principle An iris-recognition algorithm first has to identify the

approximately concentric circular outer boundaries of the iris and the pupil in a photo of an eye.

The set of pixels covering only the iris is then transformed into a bit pattern that preserves the information that is essential for a statistically meaningful comparison between two iris images.

To authenticate via identification or verification, a template created by imaging the iris is compared to a stored value template in a database.

If the Hamming Distance is below the decision threshold, a positive identification has effectively been made(HD<=0.32).

1.3 OPERATING PRINCIPLE(CNTD….) A practical problem of iris recognition is that the iris is usually partially

covered by eyelids and eyelashes. In order to reduce the false-reject risk in such cases, additional algorithms are needed to identify the locations of eyelids and eyelashes and to exclude the bits in the resulting code from the comparison operation

Human iris identification process is basically divided into four steps,

i. Localization - The inner and the outer boundaries of the iris are calculated.

ii. Normalization - Iris of different people may be captured in different size, for the same person also size may vary because of the variation in illumination and other factors.

iii. Feature extraction - Iris provides abundant texture information. A feature vector is formed which consists of the ordered sequence of features extracted from the Various representations of the iris images.

iv. Matching - The feature vectors are classified through different thresholding techniques like Hamming Distance, weight vector and winner selection, dissimilarity function, etc.

1.3 OPERATING PRINCIPLE(CNTD….)

Image for explaining Identification process

IRIS RECOGNITION SYSTEM


1.4 Advantages The iris of the eye has been described as the ideal part

of the human body for biometric identification for several reasons

It is an internal organ that is well protected against damage and wear by a highly transparent and sensitive membrane (the cornea). This distinguishes it from fingerprints, which can be difficult to recognize after years of certain types of manual labor.

The iris is mostly flat, and its geometric configuration is only controlled by two complementary muscles (the sphincter pupillae and dilator pupillae) that control the diameter of the pupil. This makes the iris shape far more predictable than, for instance, that of the face.

1.4 ADVANTAGES(CNTD) The iris has a fine texture that like fingerprints is determined

randomly during embryonic gestation . Like the fingerprint, it is very hard (if not impossible) to prove that the iris is unique. However, there are so many factors that go into the formation of these textures (the iris and fingerprint) that the chance of false matches for either is extremely low. Even genetically identical individuals have completely independent iris textures.

An iris scan is similar to taking a photograph and can be performed from about 10 cm to a few meters away. There is no need for the person to be identified to touch any equipment that has recently been touched by a stranger, thereby eliminating an objection that has been raised in some cultures against fingerprint scanners, where a finger has to touch a surface, or retinal scanning, where the eye can be brought very close to a lens (like looking into a microscope lens).The originally commercially deployed iris-recognition algorithm, John Daugman's Iris Code, has an unprecedented false match rate

1.4 ADVANTAGES(CNTD) While there are some medical and surgical procedures

that can affect the color and overall shape of the iris, the fine texture remains remarkably stable over many decades. Some Iris identificationn have succeeded over a period about 30 year.

INTRODUCTION OF IRIS RECOGNITION(CNTD….)1.5 Disadvantage Many commercial Iris scanners can be easily fooled by a high quality

image of an iris or face in place of the real thing. The scanners are often tough to adjust and can become bothersome

for multiple people of different heights to use in succession. No one is completely sure how an infrared light could potentially

damage eyesight and many feel that it should have been heavily researched before it was marketed and sold. The accuracy of scanners can be affected by changes in lighting.

Iris recognition is very difficult to perform at a distance larger than a few meters and if the person to be identified is not cooperating by holding the head still and looking into the camera. However, several academic institutions and biometric vendors are developing products that claim to be able to identify subjects at distances of up to 10 meters

As with other photographic biometric technologies, iris recognition is susceptible to poor image quality, with associated failure to enroll rates.


1.5 History The history of iris recognition goes back to mid 19th-century

when the French physician, Alphonse Bertillon, studied the use of eye color as an identifier [2].

However, it is believed that the main idea of using iris patterns for identification, the way we know it today, was first introduced by an eye surgeon, Frank Burch, in 1936 [6].

In 1987, two ophthalmologists, Flom and Safir, patented this idea and proposed it to Daugman, a professor at Harvard University, to study the possibility of developing an iris recognition algorithm.

After a few years of scientific experiments, Daugman proposed and developed a high condense iris recognition system and published the results in 1993. The proposed system then evolved and achieved better performance in time by testing and optimizing it with respect to large iris databases.

1.5 HISTORY(CNTD…..) A few years after the publication of the First algorithm by

Daugman, other researchers developed new iris recognition algorithms.

Systems presented by Wildes et al. [11], Boles and Boashash , Tisse et al., Zhu et al., Lim et al., Noh et al. and Ma et al. are some of the well-known algorithms so far.

Among these algorithms, the works done by Lim et al. and Noh et al. are also commercialized.

The algorithms developed by Wildes and Boles are suitable for verification applications because the normalization of irises is performed in the matching process and would be very time consuming in identification applications.

Although these algorithms have been successful, they still require to be improved in the accuracy and speed aspects compared to the proposed algorithm by Daugman.

2. STATE OF THE ART For instance, the developed algorithm by Daugman, which is

known as the state-of-the-art in the field of iris recognition, has initiated huge investments on the technology for more than a decade. IriScan Inc. patents the core technology of the Daugman's system and several companies such as IBM, Iridian Technologies, IrisGuard Inc., Securimetrics Inc. and Panasonic are active in providing iris recognition products and services.

Even though the Daugman system is the most successful and most well known, many other systems have been developed. The most notable include the systems of Wildes et al., Boles and Boashash, Lim et al., and Noh et al.

The algorithms by Lim et al. are used in the iris recognition system developed by the Evermedia and Senex companies. Also, the Noh et al. algorithm is used in the ‘IRIS2000’ system, sold by IriTech. These are, apart from the Daugman system, the only other known commercial implementations.

2. STATE OF THE ART (CNTD…) The Daugman system has been tested under numerous

studies, all reporting a zero failure rate. The Daugman system is claimed to be able to perfectly identify an individual, given millions of possibilities. The prototype system by Wildes et al. also reports flawless performance with 520 iris images , and the Lim et al. system attains a recognition rate of 98.4% with a database of around 6,000 eye images.

Compared with other biometric technologies, such as face, speech and finger recognition, iris recognition can easily be considered as the most reliable form of biometric technology .

However, there have been no independent trials of the technology, and source code for systems is not available. Also, there is a lack of publicly available datasets for testing and research, and the test results published have usually been produced using carefully imaged irises under favourable conditions.

3. TECHNICAL ISSUES

3.1 IMAGE ACQUISITION Why important?

One of the major challenges of automated iris recognition is to capture a high-quality image of the iris while remaining noninvasive to the human operator.

Concerns on the image acquisition rigs Obtained images with sufficient resolution and sharpness Good contrast in the interior iris pattern with proper

illumination Well centered without unduly constraining the operator Artifacts eliminated as much as possible

3.1 IMAGE ACQUISITION(CNTD…..)

Image Acquisition – Rigsa.The Daugman image-acquisition rig

b. The Wildes et al. image-acquisition rig

IMAGE ACQUISITION(CNTD……)

Image Acquisition – Results

Result Image from Wildes et al. rig -- capture the iris as part of a larger image that also contains data derived from the immediately surrounding eye region

3.1 IMAGE ACQUISITION(CNTD……)

DiscussionIn common:

Easy for a human operator to master Use video rate capture

Difference:. Operator self-position

The Daugman’s system provides the operator with live video feedback

The Wildes et al. system provides a reticle to aid the operator in positioning

3. TECHNICAL ISSUES(CNTD….)

3.2 SEGMENTATION : In segmentation, it is desired to distinguish the iris texture

from the rest of the image. An iris is normally segmented by detecting its inner (pupil) and outer (limbus) boundaries.

Well-known methods such as the Integro-differential, Hough transform and active contour models have been successful techniques in detecting the boundaries. In the following, these methods are described and some of their weaknesses are pointed out.

Iris Segmentation algorithm performed following stepsi. Reflection Removal and Iris Detectionii. Pupillary and Limbic Boundary Localization(Iris

Localization)iii. Eyelid Localizationiv. Eyelashes and shadow detection

3.2 SEGMENTATION(CNTD)

Segmentation Alogorithm


3.2.1 Daugman's Integro-differential Operator In order to localize an iris, Daugman proposed the Integro-

differential operator. The operator assumes that pupil and limbus are circular contour and performs as a circular Edge detector . Detecting the upper and lower eyelids are also performed using the Integro-differential operator by adjusting the contour search from circular to a designed arcuate. The Integro-differential is defned as

The operator pixel-wise searches throughout the raw input image, I(x,y), and obtains the blurred partial derivative of the integral over normalized circular contours in different radii.

3.2.1 DAUGMAN'S INTEGRO-DIFFERENTIAL OPERATOR(CNTD…) The pupil and limbus boundaries are expected to

maximize the contour integral derivative, where the intensity values over the circular b orders would make a sudden change. Gσ (r) is a smoothing function controlled by σ that smoothes the image intensity for a more precise search.


3.2.2 Hough Transform : First, the image intensity information is converted into a binary edge-map

Where

And

Second, the edge points vote to instantiate particular contour parameter values

3.2.2 HOUGH TRANSFORM :(CNTD….) The voting procedure of the Wildes et al. system is

realized via Hough transforms on parametric definitions of the iris boundary contours.


3.3 NORMALIZATION : Normalization refers to preparing a segmented iris image for

the feature extraction pro cess. In Cartesian co ordinates, iris images are highly a®ected by their distance and angular position with resp ect to the camera. Moreover, illumination has a direct impact on pupil size and causes non-linear variations of the iris patterns. A prop er normalization technique is exp ected to transform the iris image to comp ensate these variations.

Methematical Tools For Normalization

3.3.1 Daugman's Cartesian to Polar Transform

3.3.2 Wildes' Image Registration

3.3.3 Virtual Circles


3.4 FEATURE ENCODING AND MATCHING In order to provide accurate recognition of individuals, the

most discriminating information present in an iris pattern must be extracted. Only the significant features of the iris must be encoded so that comparisons between templates can be made.

Mathematical Tools For Feature Encoding

3.4.1 Wavelet Encoding

3.4.2 Gabor Filters

3.4.3 Log-Gabor Filters

3.4.4 Zero-crossings of the 1D wavelet

3.4.5 Haar Wavelet

3.4 FEATURE ENCODING AND MATCHING(CNTD)

The template that is generated in the feature encoding process will also need a corresponding matching metric, which gives a measure of similarity between two iris templates. This metric should give one range of values when comparing templates generated from the same eye, known as intra-class comparisons, and another range of values when comparing templates created from different irises, known as inter-class comparisons. These two cases should give distinct and separate values, so that a decision can be made with high confidence as to whether two templates are from the same iris, or from two different irises.

Mathematical Tools For Matching

3.4.6 Hamming distance

3.4.7 Weighted Euclidean Distance


3.5 IRIS IMAGE DATABASE The accuracy of the iris recognition system depends on the

image quality of the iris images. Noisy and low quality images degrade

the performance of the system.

Some Iris image database available are

I. UBIRIS

II. CASIA

III. LEA

IV. MMU

V. ICE database

4. PERFORMANCE METRICS FOR IRIS RECOGNITION

The following are used as performance metrics for Iris Recognition systems

i. False accept rate or false match rate (FAR or FMR): The probability that the system incorrectly matches the input pattern to a non-matching template in the database. It measures the percent of invalid inputs which are incorrectly accepted.

ii. False reject rate or false non-match rate (FRR or FNMR): the probability that the system fails to detect a match between the input pattern and a matching template in the database. It measures the percent of valid inputs which are incorrectly rejected.

iii. Fqual error rate or crossover error rate (EER or CER): the rate at which both accept and reject errors are equal. The value of the EER can be easily obtained from the ROC curve. The EER is a quick way to compare the accuracy of devices with different ROC curves. In general, the device with the lowest EER is most accurate.

iv. Failure to enroll rate (FTE or FER):the rate at which attempts to create a template from an input is unsuccessful. This is most commonly caused by low quality inputs

4. PERFORMANCE METRICS FOR IRIS RECOGNITION (CNTD…..)

6. Failure to capture rate (FTC): Within automatic systems, the probability that the system fails to detect a biometric input when presented correctly.

7. template capacity: The maximum number of sets of data which can be stored in the system.

5. APPLICATION OF IRIS RECOGNITION

Some Current and Future Applications of Iris Recognition

1. national border controls: the iris as a living passport.

2. computer login: the iris as a living password.

3. cell phone and other wireless-device-based authentication.

4. secure access to bank accounts at cash machines.

5. premises access control (home, office, laboratory, etc)

6. driving licenses; other personal certificates

7. forensics; birth certificates; tracing missing or wanted persons

8. credit-card authentication

9. credit-card authentication

10. anti-terrorism (e.g. security screening at airports)

11. secure financial transactions (electronic commerce, banking)

12. Biometric-Key Cryptography" (stable keys from unstable templates)

6. REFERENCES [1] S Sanderson, J. Erbetta. Authentication for secure environments based on

iris scanning technology. IEE Colloquium on Visual Biometrics, 2000. [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] R. Wildes. Iris recognition: an emerging biometric technology. Proceedings

of the IEEE, Vol. 85, No. 9, 1997. [5] J. Daugman. Biometric personal identification system based on iris

analysis. United States Patent, Patent Number: 5,291,560, 1994. [6] J. Daugman, “High Confidence Visual Recognition by a test of Statistical

Independence”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 15, No.11, pp.1148-1161,1993.

[7] R.P.Wildes, J.C.Asmuth, G.L. Green, S.C.Hsu, R.J,Kolczynski, J.R.Matey, S.E.McBride, David Sarno_ Res. Center, Princeton, NJ, “A System for Automated Iris Recognition”, Proceedings of the Second IEEE Workshop on Applications of ComputerVision,1994.

[8] W. W. Boles and B. Boashash , “A Human Identification Technique Using Images of the Iris and Wavelet Transform”, IEEE Transactions On Signal Processing, Vol. 46, No. 4, April 1998.

6. REFERENCES (CNTD……..) [9] S. Lim, K. Lee, O. Byeon, T. Kim. Efficient iris recognition through

improvement of feature vector and classifier. ETRI Journal, Vol. 23, No. 2, Korea, 2001.

[10 ]S. Noh, K. Pae, C. Lee, J. Kim. Multiresolution independent component analysis for iris identification. The 2002 International Technical Conference on Circuits/Systems,Computers and Communications, Phuket, Thailand, 2002.

[11] Y. Zhu, T. Tan, Y. Wang. Biometric personal identification based on irispatterns. Proceedings of the 15th International Conference on Pattern Recognition, Spain, Vol. 2, 2000.

[12] C. Tisse, L. Martin, L. Torres, M. Robert. Person identification technique using human iris

recognition. International Conference on Vision Interface, Canada, 2002. [13]Chinese Academy of Sciences – Institute of Automation. Database of 756

Greyscale Eye Images. http://www.sinobiometrics.com Version 1.0, 2003. [14] C. Barry, N. Ritter. Database of 120 Greyscale Eye Images. Lions Eye

Institute, Perth Western Australia. [15] W. Kong, D. Zhang. Accurate iris segmentation based on novel reflection

and eyelashdetection model. Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, 2001.

http://www.sinobiometrics.com/

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ppt on iris recognition 1

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