• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Iris based Human Identification
 

Iris based Human Identification

on

  • 4,744 views

This report will give idea of key steps in developing an algorithm for \’Iris based Recognition system\’.Experimental observations as well are also shown.

This report will give idea of key steps in developing an algorithm for \’Iris based Recognition system\’.Experimental observations as well are also shown.

Statistics

Views

Total Views
4,744
Views on SlideShare
4,727
Embed Views
17

Actions

Likes
7
Downloads
0
Comments
4

2 Embeds 17

http://www.linkedin.com 16
https://www.linkedin.com 1

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel

14 of 4 previous next Post a comment

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Iris based Human Identification Iris based Human Identification Document Transcript

    • A PROJECT REPORT ON “IRIS BASED HUMAN RECOGNITION SYSTEM” SUBMITTED BY: MR. DESAI GAURAV S. MR. JOSHI SUSHIL D. MS. IYER ANANDHI G. MR.WAZALWAR DHAWAL S. (BE. EXTC) GUIDED BY: MR. R.R. MANTHALKAR SGGS INSTITUTE OF ENGINEERING AND TECHNOLOGY, VISHNUPURI, NANDED SWAMI RAMANAND TEERTH MARATHWADA UNIVERSITY, NANDED 1
    • CERTIFICATE THIS IS TO CERTIFY THAT THE PROJECT REPORT ENTITILED “IRIS BASED HUMAN RECOGNITION SYSTEM” SUBMITED BY MR. DESAI GAURAV S. MR. JOSHI SUSHIL D. MS. IYER ANANDHI G. MR.WAZALWAR DHAWAL S. (BE. EXTC) HAS BEEN COMPLETED AS PER THE REQUIREMENT OF “SWAMI RAMANAND TEERTH MARATHWADA UNIVERSITY, NANDED”. IN PARTIAL FULFILMENT OF THE DEGREE B.E. (Electronic and Telecommunication Engineering) FOR THE ACADEMIC YEAR 2006-2007. GUIDE HEAD OF DEPT. DR. R.R MANTHALKAR Prof. A.N .KAMTHANE 2
    • INDEX • ACKNOWLEDGEMENT………………………………........4 • ABSTRACT………………………………………………......5 • LIST OF FIGURES……………………………………..........6 • INTRODUCTION TO BIOMETRICS ………………………7 o ADVANTAGES OF BIOMETRICS………………………………8 o COMMON BIOMETRIC PATTERNS……………………………9 o THE HUMAN IRIS………………………………………………..10 • PROJECT INTRODUCTION…………………………...…..14 o OBJECTIVE………………………………………………………14 o BASIC ASSUMPTIONS………………………………………….14 o DATABASE CHARACTERISTICS……………………………...15 • IMAGE SEGMENTATION ………………………….…….16 o AVAILABLE TECHNIQUE…………………………….……….16 o OUR APPROACH……………………………………….……….18 • IMAGE NORMALIZATION ………………………………23 o BASIC MOTIVATION…………………………………..………23 o OUR APPROACH……………………………………… ……….24 • FEATURE EXTRACTION ………………………………...26 o BASIC TECHNIQUE……………………………………………26 o OUR APPROACH…………………………………………….....29 • MATCHING …………………………………….…………31 o DIFFERENT DISTANCE METRICS…………………………..31 o OUR APPROACH………………………………………………34 • ENROLLMENT AND IDENTIFICATION…….…….........35 o ENROLLMENT PHASE………………………………………..35 o IDENTIFICATION PHASE………………………………….....36 3
    • • CONCLUSION …………………………………………….37  SUMMARY OF WORK………………………………37  SUGGESTED IMPROVEMENTS…………………….38 • GRAPHIC USER INTERFACE……………………...…….39 • REFERENCES …………………………………………......43 ACKNOWLEDGEMENT 4
    • For the accomplishment of any Endeavour the most important factor besides Hard-work & Dedication is the Proper Guidance. Just as a light-house provides guidance to the sea voyagers even in the extreme stormy conditions the same job has been done by our guide Dr. R.R. Manthalkar. We heartily thank him for his timely support & guidance. His keen interest & enthusiasm in executing the project has always been a source of inspiration for us. We would like to thank our Head of department Prof. A.N Kamthane and also Dr. M.B.kokare for their moral support & encouragement. We would also take this opportunity to extend our sincere thanks to all the other professors of our department for their direct or indirect help extended by them which was a cap in the feather of our efforts. We would also like to thank all our friends who also played an instrumental role in the completion of our project. Last but not the least we would like to thank our parents who always support us in our efforts. MR. DESAI GAURAV S. MR. JOSHI SUSHIL D. MS. IYER ANANDHI G. MR.WAZALWAR DHAWAL S. (BE. EXTC) 5
    • ABSTRACT The successful implementation of any system is largely determined by its reliability, authenticity & the amount of secrecy it provides. In today’s highly techno world where security & privacy are the concerns of prime importance the crucial systems must employ techniques to achieve this. Our project is just a small step towards this. The Iris based system can cope up with a lot of the individual biological variations & still provide the identification system with much accuracy & reliability. In this project we have designed system which involves recognition of a person using IRIS as biometric parameter. We have first segmented pupil & iris structure from the original eye image. Then we have normalized it to build a feature vector which characterizes each iris distinctly. This feature vector is then used for matching among various templates & identifies the individual. The work provided in this project is mainly aimed at providing a recognition system in order to verify the uniqueness of the human iris and also its performance as a biometric. The paper has implementation of all the algorithms on the CASIA database. The various results of the different implementations and their accuracies have been tested in this paper. All in all this paper is a sincere effort in suggesting an efficient system for the implementation of the Human Identification system based on IRIS recognition. LIST OF FIGURES 6
    • • Fig no. 1.3.1 Picture of Human Eye……………………….12 • Fig no. 3.1.1 Demonstration of Hough Transform………...17 • Fig no.3.2.1 Fully Segmented Iris Image………………....21 • Fig no 4.1.1 Daugman`s Rubber sheet model…………....23 • Fig no. 4.2.1 Fully and Enhanced Normalized Image……..24 • Fig no. 5.1.1 2 Level Decomposition of Wavelet………….26 • Fig no. 5.2.1 Application of db2 Wavelet to eye image…...29 • Fig no. 7.1.1 General Iris Identification System ………….34 CHAPTER 1 INTRODUCTION TO BIOMETRICS 7
    • In this vastly interconnected society establishing the true identity of the person is becoming the most critical issue. The questions like “Is she really who she claims to be?”, “Is this person authorized to use this facility?” or “Is he in the watch list posted by the government?” are routinely being asked in a variety of scenarios ranging from issuing a driver’s license to gaining entry into a country. With the advent of the newer networking technologies the sharing of vast audio & video resources has become much easier but has raised brows over the issues concerning the security in the transactions. The need for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking , communication and mobility. Biometrics, defined as a science of recognizing an individual based on her physiological, biological or behavioral traits, is beginning to gain acceptance as a legitimate method for determining an individual’s identity. Biometrics aims to accurately identify each individual using various physiological or behavioral characteristics such as fingerprints, Face, iris, retina, gait, palm- prints and hand geometry. The developments in science and technology have made it possible to use biometrics in applications where it is required to establish or confirm the identity of individuals. Applications such as passenger control in airports, access control in restricted areas, border control, database access and financial services are some of the examples where the biometric technology has been applied for more reliable identification and verification. In recent years, biometric identity cards and passports have been issued in some countries based on iris. Fingerprint and face recognition technologies to improve border control process and simplify passenger travel at the airports. In the field of financial services, biometric technology has shown a great potential in offering more comfort to customers while increasing their security. Although there are still some concerns about using biometrics in the mass consumer applications due to 8
    • information protection issues, it is believed that the technology will find its way to be widely used in many different applications. 1.1 ADVANTAGES OF BIOMETRICS: 1. It links an event to a particular individual, not just to a password or token. 2. It is very convenient from user friendliness point of view since there is nothing to remember, unlike in the case of passwords or some code words. 3. It can’t be guessed, stolen, shared, lost or forgotten. 4. It prevents impersonation by protecting against identity theft and providing higher degree of non- repudiation. 5. It enhances privacy by protecting against unauthorized access to personal information. A good biometric is characterized by use of a feature that is highly unique – so that the chance of any two people having the same characteristic will be minimal, stable – so that the feature does not change over time, and be easily captured – in order to provide convenience to the user, and prevent misrepresentation of the feature. 1.2 COMMON BIOMETRIC PATTERNS 1. Finger print Recognition Features: a. Measures characteristics associated with the friction ridge pattern on the fingertip. b. General ease and speed of use. c. Supports both 1:1 verification and 1:N applications Considerations: a. Ridge patterns may get affected due to accidents or aging effects. 9
    • b. Requires Physical contact with sensor. 2. Facial Recognition Features: a. Analyzes geometry of the face or the relative distance between features. b. No physical contact required. c. Supports both 1:1 verification and 1:N identification applications. Considerations: a. Can be affected by surrounding lightning conditions. b. Appearance may change over time. 3. Hand Geometry Features: a. Measures dimensions of hand, including shape and length of finger. b. Very low failure to enroll rate. c. Rugged. Considerations: a. Suitable only for 1:1 contexts. 4. Speech Recognition Features: a. Compares live speech with previously created speech model of person’s voice. b. Measures pitch, cadence and tone to create voice print cadence. Considerations: a. Background Noise can interfere b. Suitable only for 1:1 contexts. 10
    • 1.3 THE HUMAN IRIS The human iris is rich in features which can be used to quantitatively and positively distinguish one eye from another. The iris contains many collagenous fibers, contraction furrows, coronas, crypts, color, serpentine vasculature, striations, freckles, rifts, and pits. Measuring the patterns of these features and their spatial relationships to each other provides other quantifiable parameters useful to the identification process. The iris is unique because of the chaotic morphogenesis of that organ. To quote Dr. John Daugman, “An advantage the iris shares with fingerprints is the chaotic morphogenesis of its minutiae. The iris texture has chaotic dimension because its details depend on initial conditions in embryonic genetic expression; yet, the limitation of partial genetic diffusion (beyond expression of form, function, color and general textural quality), ensures that even identical twins have uncorrelated iris minutiae. Thus the uniqueness of every iris, including the pair possessed by one individual, parallels the uniqueness of every fingerprint regardless of whether there is a common genome.” Given this, the statistical probability that two irises would be exactly the same is estimated at 1 in 10^72. 1.3. a. STABLITY OF IRIS Notwithstanding its delicate nature, the iris is protected behind eyelid, cornea, aqueous humor, and frequently eyeglasses or contact lenses (which have negligible effect on the recognition process). An iris is not normally contaminated with foreign material, and human instinct being what it is, the iris, or eye, is one of the most carefully protected organs in one’s body. In this environment, and not subject to deleterious effects of aging, the features of the iris remain stable and fixed from about one year of age until death. 11
    • 1.3. b. STRUCTURE OF IRIS Fig no. 1.3.1 Picture of Human Eye The iris is a thin circular diaphragm, which lies between the cornea and the lens of the human eye. A front-on view of the iris is shown in Figure. 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 color of the iris. The externally visible surface of the multi-layered iris contains two zones, which often differ in color. An outer ciliary’s zone and an 12
    • inner pupillary zone, and these two zones are divided by the collarets – which appears as a zigzag pattern. 1.3. c. ADVANTAGES OF USING IRIS PATTERN 1. Highly protected internal organ of the eye. 2. Iris patterns possess a high degree of randomness. 3. Patterns are apparently stable throughout life. 4. Comparatively fast matching technique. 13
    • CHAPTER 2 PROJECT INTRODUCTION 2.1. OBJECTIVE The objective will be to implement an open-source iris recognition system in order to verify the claimed performance of the technology. Our main system consists of many subsystems. Important steps involved can be demonstrated as follows: 1. Segmentation: Locates the iris region in the eye image by eliminating the unwanted parts. 2. Normalization Creating a dimensionally consistent representation of iris region. 3. Feature Encoding and Matching Creating a template containing only the most discriminating features of the iris and matching it. So our main aim is to implement best possible algorithm for each step and obtain required degree of accuracy. 2.2 BASIC ASSUMPTIONS a) Methods for image segmentation and feature extraction will assume all patterns have the same rotation angle. b) The Iris and the pupil regions are assumed to be perfectly concentric circles. c) The pupil region has a constant intensity throughout. d) Of the available seven images of a subject four are considered as principle images while three are considered for test images. e) We have resized normalized image of each of the iris into a vector of constant dimensions irrespective of their original size. 14
    • 2.3. DATABASE CHARACTERISTICS This paper is based on the CASIA image database. Its ethnical distribution is composed mainly of Asians. Each iris class is composed of 7 samples taken in two sessions, three in the first session and four in the second. Sessions were taken with an interval of one month. Images are 320x280 pixels gray scale taken by a digital optical sensor designed by NLPR (National Laboratory of Pattern Recognition – Chinese Academy of Sciences). There are 108 classes or irises in a total of 756 iris images.Each of the iris images is preprocessed to eliminate the effect of the illumination variations and other noise effects. 15
    • CHAPTER 3 IMAGE SEGMENTATION The main aim of the segmentation step is to distinguish an iris texture from the rest of the eye image. Properly detecting the inner and outer boundaries of iris texture is significantly important in all iris recognition systems. The segmentation is a crucial step in that any false representation here may corrupt the resulting template in poor recognition rates. The iris is an annular portion between the pupil (inner) and the sclera (outer boundary). The iris regions can be approximated by two circles, one for the iris/sclera boundary and another, interior to the first, for the iris/pupil boundary. The eyelids and eyelashes normally occlude the upper and lower parts of the iris region. Also, specular reflections can occur within the iris region corrupting the iris pattern. However, our project work is mainly focused on CASIA iris database which do not contain specular reflections due to the use of near infra-red light for illumination. The success of the segmentation depends upon the imaging quality of the eye image. Imaging of the iris must acquire sufficient detail for recognition while being minimally invasive to the operator. 3.1 Available Techniques a. Daugman`s Integro Differential Operator Daugman proposed a method by making use of first derivatives of image intensity to signal the location of edges that corresponds to the location of edges that corresponds to the borders of the iris. The notion is that the magnitude of the derivative across an imaged border will show a local maximum due to the local change of image intensity. The limbus and pupil are modeled with circular contours. The expected configuration 16
    • of model components is used to fine tune the image intensity derivative information. Daugman`s operator is expressed as I(x, y) is an image containing an eye The operator searches over the image domain (x, y) for the maximum in blurred partial derivative with respect to increasing radius r of the normalized contour integral of I(x, y) along a circular arc ds of radius r and center coordinates (x0, y0) . Is smoothing function Operator serves and behaves as circular edge detector blurred at a scale by σ, which searches iteratively for maximum contour integral derivative with increasing radius at successively finer scales of analysis through the three parameter space of center coordinates and radius (x0, y0, r) defining a path of contour integration. b. Hough Transform This is a standard computer algorithm that can be used to determine the parameters of single geometric objects such as lines and circles, present in an image. Circular Hough Transform is used to deduce the radius and center coordinates of the pupil and iris regions, while to detect the eyelids, parabolic Hough Transform is used.Hough Transform can be demonstrated as follows: Fig. no. 3.1.1 Demonstration of Hough Transform 17
    • These edge maps along with determination of some appropriate points in Hough space will give us required parameters. Some problems in use of Hough Transform are: 1. It is difficult to determine the threshold values to be chosen for edge detection resulting in critical edge points being removed sometimes, resulting in failure to detect circles/arcs. 2. This approach is computationally intensive due to its brute force nature and thus may not be suitable for real time applications. c. Other Methods for Segmentation Iris localization proposed by Tisse. et al is combination of Integro differential and the Hough Transform. The Hough Transform is used for a quick guess of the pupil center and then the integro differential is used to accurately locate pupil and limbus using a smaller search space. Lim et al localize pupil and limbus by providing an edge map of the intensity values of the image. The center of the pupil is then chosen using a bisection method that passes perpendicular lines from every two points on the edge map. The center point is then obtained by voting the point that has the largest number of line crossovers. The pupil and limbus boundaries are then selected by increasing the radius of a virtual circle with selected center point and choosing the two radii that have the maximum number of edges by the virtual circle as the pupil and limbus radii. 3.2 Our Approach Our approach is mainly divided into four parts as listed below: 1. Separation of Pupil Region from Eye image. 2. Determination of Pupil Coordinates 3. Determination of Iris Edges based on the above two steps 4. Removing the unwanted part and getting the segmented part 18
    • a. Separation of Pupil Region CASIA Database images have been preprocessed and each has constant intensity pupil region. Firstly we determine this constant value so that we can use it as a threshold value to separate pupil region from eye image. For this we employ following steps: • First, We obtain mean of the image (say ’m’) • Then we scan the image completely to determine the regions having same pixel value consecutively for 15 times. • Sometimes, Camera effect may develop bright constant intensity regions on the eyelids. To avoid selecting this region value as threshold value, we compare each region value with the obtained mean (here ‘m’) and select that value which is less than the mean value. • After getting the threshold value, we stop scanning the image. • Sometimes, eyelashes also satisfy the threshold condition, but have much smaller area than pupil area. Using this knowledge and concept of 8 connected pixels, we can cycle through all regions and apply the following conditions For each region R If Area(R) <1300,Set all pixels of R to 0 • To this finally obtained pupil image, some statistical MATLAB functions are applied and it’s Center Coordinates and radius is determined. b. Determination of Pupil Coordinates This function is mainly used to determine the edges of pupil. Here, we take the help of MATLAB ‘find’ function to detect the abrupt change in the image intensity. This function also helps us to verify the previously determined Pupil parameters with the help of following formula: Center = (|Right/Top Edge - left/Bottom Edge|)/2 Coordinates coordinates Coordinates 19
    • c. Determination of iris edges This is used to obtain the contour of the iris. Here, we assume that iris and pupil are concentric circular regions. It takes into consideration that areas of the iris at the right and left of the pupil are the ones that most often present visible to data extraction. The areas above and below the pupil also carry unique information, but it is very common that they are totally or partially occluded by eyelash or eyelid. Firstly, we enhance the iris image so that edges can be seen more prominently and their detection will become simpler. For this we employ Histogram equalization method which gives us an image with increased dynamic range, which will tend to have higher contrast. Then we follow certain steps as listed below to get the iris edges: • Firstly, we get the pupil edges from previously explained functions. • Then, we start the process with 30 pixels left to the left edge of pupil (say this point is ‘a’), so as to avoid abrupt intensity changes in collarette region, which may mislead our algorithm. We form a vector containing image intensity at that point ‘a’ and 4 pixels above and below it. Mean of this vector is obtained (say it is m1). • Another vector containing image intensity at point ‘a-1’ and 3 points above and below it is obtained. Mean of this vector is obtained (say it is m2). • Now, We compute ‘m’, where m=|m1- m2| • If m>. 04, there may be abrupt change in image intensity. • To avoid false detection which may cause due to iris region getting corrupted by some reasons, we obtain similar vector for ‘a- 2’also and calculate its mean and store it in m2. • Again step 5 is repeated and if condition is satisfied for 10 times consecutively we conclude that this is the left edge of 20
    • the iris and stop further calculations. We calculate this edge’s distance from pupil center and thus we get iris radius towards left i.e. r1. • The above steps are repeated for right part also. Here, the exception is that we start from 30 pixels right to the right most pupil edge. This gives radius towards right i.e. r2. • We take the larger value amongst r1 and r2 so as to avoid any data loss. d. Removal of unwanted part from the image Now, the above data so far obtained can be used to eliminate portions other than iris region. This is done by approximating Iris as a circle with center at pupil center. Any region outside this circle is deleted and then finally we get the segmented image as shown: Fig no. 3.2.1 Fully Segmented Iris Image 21
    • CHAPTER 4 NORMALIZATION The main aim of normalization is to transform the iris region into fixed dimensions, in order to null the effect of dimensional inconsistencies. These inconsistencies are the result of changes that occur in image distance and angular position with respect to the camera. Other sources of inconsistencies include stretching of the iris caused by pupil dilation from varying levels of illumination, head tilt and rotation of eye within the eye socket. Normalization is a very important step in that success of subsequent steps depends largely on the accuracy of this step. 4.1 BASIC MOTIVATION Daugman`s Rubber Sheet Model This method is based on remapping each point within the iris region to a pair of polar coordinates (r, θ) where r is on the interval [0 1] and θ is angle in [0 2Π]. The proposed method is capable of compensating the unwanted variations due to distance of eye from the camera (scale) and its position with respect to the camera (Translation). The Cartesian to polar transform is defined as: Where, I(x, y) is the iris region image,(x, y) are the original Cartesian coordinates ,(r, θ) are the corresponding normalized polar 22
    • coordinates and , and , are the pupil and iris boundaries along θ direction. Pictorial Depiction of this model is as shown Fig no. 4.1.1 Daugman`s rubber sheet model This Rubber sheet model takes into account pupil dilation and size inconsistencies in order to produce a normalized with constant dimensions. However, this model does not compensate for rotational inconsistencies. In Daugman`s system, rotation is accounted for during matching by shifting the iris template in θ direction until iris templates are aligned. 4.2 OUR APPROACH: Our algorithm involves the polar conversion of the iris image. The polar conversion of the iris image accounts for all the rotational inconsistencies in the iris image. In this algorithm we map only the annular iris region coordinates into a fixed dimension vector thus eliminating the unwanted and redundant pupil & other non iris regions. The algorithm can be explained as follows: 1. Finding out incremental values of the radius and the rotation angle. 23
    • 2. Creating a pseudo polar mesh grid of fixed dimensions using the incremental values and the radius of the iris region. 3. Separating only the annular iris ring thus eliminating pupil & other regions. 4. Conversions of each coordinate of the iris region into its equivalent polar coordinate using the relations. 5. Mapping of the each of the iris coordinate onto the polar grid a using linear interpolation. 6. The iris region is mapped into a vector of fixed dimensions of size 100x360 which can be seen as shown below Fig no.4.2.1 Fully and enhanced Normalized Image 24
    • CHAPTER 5 FEATURE EXTRACTION The normalized image is used to extract the unique features in the iris image. Each iris is characterized by its unique collarette pattern, iris variation patterns. Hence it is very essential to extract these features to represent each iris distinctively. Most Iris recognition systems make use of a band pass decomposition of iris image to create a biometric template. This step is responsible of extracting the patterns of iris taking into account the correlation between the adjacent pixels. 5.1 BASIC TECHNIQUE a. THEORY OF WAVELETS Wavelets are the basis functions wjk(t) in continuous time. A basis is a set of linearly independent functions that can be used to produce all admissible functions f(t). F(t)=combination of basis functions = ∑bjk wjk(t) The special feature of the wavelet is that all the functions are constructed from a single mother wavelet w(t). Wavelet transform overcomes the resolution problem of the traditional Fourier transform techniques by using a variable length window. Techniques like short time fourier transform used to divide the signal into short time domains. The fourier transform of the signal was then computed in each domain around a particular center frequency. However this led to the time resolution problem which led to the wavelet approach. Analysis windows of different lengths are used for different frequencies: Analysis of high frequenciesè Use narrower windows for better time resolution Analysis of low frequencies è Use wider windows for better frequency resolution 25
    • This works well, if the signal to be analyzed mainly consists of slowly varying characteristics with occasional short high frequency bursts. The function used to window the signal is called the wavelet ψ ψ 1 ∗ t −τ  CWT x (τ , s ) = Ψ (τ , s ) = x ∫ x( t )ψ  s dt s t   ~ x[n yhigh [k ] = ∑ x[n] g[− n + 2k ] ∑ yhigh [k ] ⋅ g[−n + 2k ] x[n] k ] n ~ G G + 2 2 ~ 2 ~ 2 H G 2 2 G + H ~ 2 2 ~ ylow[k ] = ∑ x[n] h[− n + 2k ] H H ∑ yhigh [k ] ⋅ g[−n + 2k ] n k Decomposition Reconstruction G 2 H 2 Fig no. 5.1.1 2 Level Decomposition of Wavelet b. Discrete Wavelet Transform The DWT analyzes the signal at different frequency bands with different resolutions by decomposing the signal into coarse approximation and detail information. DWT employs two sets of functions, called scaling functions and wavelet functions, which are associated with low pass and high pass filters, respectively. The decomposition of signal into different frequency bands is simply obtained by successive high pass and low pass filtering of the time domain signal. The original signal x[n] is first passed through a half band high pass filter g[n] and low pass filter h[n]. After the filtering, half of the samples can be eliminated 26
    • according to the Nyquist`s rule, since the signal now has a highest frequency of Π/2 radians instead Π. The signal can therefore be sub sampled by discarding every other sample. This constitutes one level of decomposition and can mathematically be expressed as follows: Yhigh[k] =Σx[n].g[2k - n] Ylow[k] =Σx[n].h[2k - n] for all n; Where Yhigh[k] and Ylow[k] are the outputs of the high pass and low pass filters, respectively, after sub sampling by 2. This decomposition halves the time resolution since only half the number of samples characterizes the entire signal. However, this operation doubles the frequency resolution since the frequency band of signal now spans only half the previous frequency band, effectively reducing the uncertainty in the frequency by half. The above procedure, which is also known as the sub band coding, can be repeated for further decomposition. At every level, the filtering and sub sampling will result in half the number of samples (and hence half the time resolution) and half the frequency bands spanned (and hence double the frequency resolution). c. VARIOUS TYPES OF WAVELETS 1. Haar Wavelet This uses the wavelet transform to extract features from the iris region. Both the Gabor transform and the Haar wavelet are considered as the mother wavelet. From multi-dimensionally filtering, a feature vector with 87 dimensions is computed. Since each dimension has a real value ranging from -1.0 to +1.0, the feature vector is sign quantized so that any positive value is represented by 1, and negative value as 0. This results in a compact biometric template consisting of only 87 bits. 27
    • 2. Daubechies Wavelet These are a family of orthogonal wavelets defining a DWT and characterized by a maximal number of vanishing moments for some given support. With each wavelet type of this class, there is a scaling function (also called as Father Wavelet) which an orthogonal multi resolution analysis. An orthogonal wavelet is a wavelet where the associated wavelet transform is orthogonal i.e. the inverse wavelet transform is ad joint of the wavelet transform. In general the Daubechies wavelets are chosen to have the highest number ‘A’ of vanishing moments (This does not imply the best smoothness)for given support width N=2A and among the 2^(A-1) possible solutions, the one is chosen whose scaling filter has external phase. These wavelets are widely used in solving broad range of problems, example similarity property of a signal, signal discontinuities, etc. Daubechies orthogonal wavelet D2-D0 is commonly used. The index number refers to the number N of coefficients. Each wavelet has a number of zero moments or vanishing moments equal to half the number of coefficients. 5.2 OUR APPROACH: We have applied a 5- level Wavelet transform and have analyzed the results for all types of Wavelets like Haar, Daubechies and Bior. We found the best results for Daubechies that to for db2 type. In each case, feature size varied depending upon the type wavelet applied. Normally, the final template size should not be a function of type of wavelet, but since our image size is not exactly in terms of powers of two (i.e it is 100x360 ) and so subsequent desampling will result in loss of some pixels. This causes template size to be a function of type of Wavelet and also on the number of levels. Following figure shows 4- level DB2 Wavelet applied to the eye image. 28
    • Fig no. 5.2.1 Application of db2 Wavelet to Eye image The results obtained by applying different types of Wavelet are shown separately in Results section. 29
    • CHAPTER 6 MATCHING For comparing the templates obtained by feature extraction process, there are number of design metrics available. Some of them are explained: 6.1 Different Distance Metrics If x and yare two d-dimensional feature vectors of database image and query image respectively, then the distance metrics are defined as: a. Euclidean or L2 metric : Euclidean distance is not always the best metric. The fact that the distances in each dimension are squared before summation, places great emphasis on those features for which the dissimilarity is large. b. Weighted Euclidean distance metric: The weighted Euclidean distance (WED) can be used to compare two templates, especially if the template is composed of integer values. The weighting Euclidean distance gives a measure of how similar a collection of values are between two templates. This metric is specified as 30
    • where is the feature of the unknown iris, and is the feature of iris template, ,and is the standard deviation of the feature in iris template . The unknown iris template is found to match iris template , when WED is a minimum at . c. The Manhattan or LI metric: The Manhattan distance metric uses using sum of the absolute differences in each feature, rather than their squares, as the overall measure of dissimilarity. It is obvious that the distance of an image from itself is zero. The distances are then stored in increasing order and closest sets of patterns are then retrieved. In ideal case all the top 16 retrievals are from same large image. The performance is measured in terms of the average retrieval rate, which is defined as the average percentage of patterns belonging to the same image as the query pattern in the top 16 matches. d. Hamming Distance metric: The Hamming distance gives a measure of how many bits are the same between two bit patterns. Using the Hamming distance of two bit patterns, a decision can be made as to whether the two patterns were generated from different irises or from the same one. In comparing the bit patterns X and Y, the Hamming distance, HD, is defined as the sum of disagreeing bits (sum of the exclusive-OR between X and Y) over N, the total number of bits in the bit pattern. 31
    • Since an individual iris region contains features with high degrees of freedom, each iris region will produce a bit-pattern which is independent to that produced by another iris, on the other hand, two iris codes produced from the same iris will be highly correlated. If two bits patterns are completely independent, such as iris templates generated from different irises, the Hamming distance between the two patterns should equal 0.5. This occurs because independence implies the two bit patterns will be totally random, so there is 0.5 chance of setting any bit to 1, and vice versa. Therefore, half of the bits will agree and half will disagree between the two patterns. If two patterns are derived from the same iris, the Hamming distance between them will be close to 0.0, since they are highly correlated and the bits should agree between the two iris codes. e. Canberra distance metric: The Canberra distance metric is given by In these metric equations the numerator signifies the difference and denominator normalizes the difference. Thus distance values will never exceed one, being equal to one whenever either of the attributes is zero. Thus it would seem to be a good expression to use, which avoids scaling effect. 32
    • 6.2 Our approach: Running the experiment with different distance metric on same set of images we were able to find which distance metric gives best result. The Canberra distance metric performed exceptionally well than other distance metric. The fact is that in this metrics equation the numerator signifies the difference and denominator normalizes the difference. Thus distance values will never exceed one, being equal to one whenever either of the attributes is zero. 33
    • CHAPTER 7 ENROLLMENT AND IDENTIFICATION 7.1 ENROLLMENT PHASE a. PROBLEM STATEMENT The enrollment phase in any biometric system is required to validate its users. If an authentic person supposed to use the system does not have his images registered in the database then every time the person tries enter the system the biometric system rejects him and refrains from providing access to the system to that person. Hence a person worthy of access is denied the service. In such cases it becomes necessary to first enroll the eligible persons into the database for their future identification. Our enrollment algorithm is basically aimed at solving this problem. Fig no. 7.1.1 General Iris identification system b. STEPS: The enrollment phase of the project involves checking whether any of the subject is already enrolled in the database or not and if not getting the subject enrolled. Here a basic graphic user interface window is implemented. Of the available seven image s of a subject three are considered as principle images and rest four are considered 34
    • for testing the enrollment. The basic steps are highlighted as follows: • A test image of a subject is fed to the algorithm. • The image is subjected to series of steps of segmentation, normalization, and feature extraction to form a feature vector template. • This is used to compute the distance between the template and combined pattern of all the subjects using the Canberra distance metric. • The distances are stored in a vector. • This is then sorted in ascending order. • The minimum distance indicates the class of images to which this test image belongs to. • The test image is then once again compared with the principle images of the class and the distance between them is computed. • If the above distance is less than the one obtained for the combined pattern of that class then we can establish that the image indeed belongs to that class and hence is already enrolled. • If the distance computed in the step seven is greater than the combined distance we conclude that the image does not belong to that class and is not enrolled. • In case the test image is not enrolled then it is enrolled by storing all the patterns of the subjects in the database. 7.2 IDENTIFICATION PHASE a. PROBLEM STATEMENT This phase verifies the claimed identity of the individual. In this phase a person who wants to get access to a system claims his identity as being a particular authentic person. The biometric system verifies for the claim and establishes whether he is indeed the person 35
    • who he claims to be or an imposter.The following algorithm solves this problem. b. STEPS This algorithm is implemented using a graphic user interface. • A test image is input through the graphic interface. • The image is subjected through the processing steps such as segmentation, normalization and feature extraction to the retrieve the feature vector. • This is used to compute the distance between the template and combined pattern of all the subjects using the Canberra distance metric. • The distances are stored in a vector. • This is then sorted in ascending order. • The minimum distance indicates the class of images to which this test image belongs to. • The test image is then once again compared with the principle images of the class and the distance between them is computed. • If the above distance is less than the one obtained for the combined pattern of that class then we can establish that the image indeed belongs to that class and hence the claimed identity of the person is established. • If the distance computed in the step seven is greater than the combined distance we conclude that the image does not belong to that class and hence the person is an imposter. CHAPTER 8 36
    • CONCLUSION 8.1 Summary of work This project work presents an Iris recognition system, which was tested on CASIA Iris Image Database, in order to verify the claimed performance of iris recognition technology. Analysis of the developed system has revealed a number of interesting conclusions. Accuracy of Biometric identification systems is specified in terms of FAR (False Acceptance Rate) and FRR (False Rejection Rate) . FAR measures how often a non –authorized user, who should not be granted access, is falsely recognized, while FRR measures how often an authorized user, who should have been granted access is not recognized. a. Results for applying different types of Wavelets Following is the table which shows the effect of applying various types of the Wavelet Wavelet Vector Size FAR FRR No. of Faulty Type Subjects Db3 764X1 5 130 74 Db1 418X1 17 87 52 Db4 1020X1 15 133 71 Bior 4.4 1047X1 4 87 52 Bior 1.1 418X1 17 87 52 Db2 477X1 1 73 30 Thus we can conclude that db2 gives the best possible results and so we have selected this type of Wavelet. b. Results for Overall Database 37
    • Thus after analyzing the results over complete database we found following results. False Acceptance Rate=1 False Rejection Rate=73 Number of Faulty Subjects= 41 Thus, our project has very small FAR which is what a reliable biometric system should possess. However, comparatively our FRR is quite high and some measures can be taken to reduce it. 8.2 Suggested Improvements 1. Segmentation is a very crucial step in Iris Recognition Systems and so to whatever extent its accuracy can be increased will result in more improved results. To improve segmentation algorithm, a more elaborate eyelid and eyelash detection system could be implemented. 2. Presently, our Template size varies with the type of Wavelet applied. So, some measures can be taken to avoid this effect. Our aim of project was mainly to have a very low FAR, which we have achieved to a large extent. Also, our segmentation part has a very high accuracy and has worked satisfactorily over entire CASIA database. Since, we had restricted ourselves to software part implementation and not taken too much hardware implications in too concern, our efforts have resulted in efficient Recognition System. CHAPTER 9 38
    • GUI Using MATLAB Since MATLAB provides a very easy way of implementing a GUI (Graphical User Interface), we have prepared a GUI which gives a general idea of the work we have done. Following are some of its details: 1. Starting Window: This is starting window of our GUI which provides link to various phases of our project. The Results for overall can be obtained by clicking on 1st pushbutton. To have Individual analysis of the database, we have provided one option in the form of 2nd pushbutton. Different steps in pattern formation can be seen by clicking on 3rd pushbutton. 2. Results for Complete Database 39
    • The above window shows the overall database results. The update facility is provided so as to again run the code over entire database, in case any changes made in the original code. 3. Individual Enrollment 40
    • This window allows us to individually analyze each person by enrolling that person separately and then check if we get proper results against each image of that person in database. Here, we have used four images for pattern formation and then keep remaining three images as test images. 4. Steps in Pattern formation 41
    • This window provides a detailed analysis of pattern formation. Here, we have made provisions to see Pupil separation, Iris segmentation and also Normalized Image for any image in database. 42
    • CHAPTER 10 REFERENCES 1. JOURNALS AND CONFERENCE PAPERS • Daugman, J., How Iris Recognition Works, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, Number 1, January 2004. • Masek L., Recognition of Human Iris Patterns for Biometric Identification, [http://www.csse.uwa.edu.au/~pk/studentprojects/libor]. • CASIA iris image database, Institute of Automation, Chinese Academy of Sciences, [http://www.sinobiometrics.com] • R. Wildes. Iris recognition: an emerging biometric technology. Proceedings of the IEEE, Vol. 85, No. 9,1997. • Daugman J., Biometric Personal Identification System based on Iris Analysis,United States Patent, Patent No. 5,291,560, March 1994. • J. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 11, 1993. • W.W.Boles, A security system based on human Iris Identification using Wavelet Transform ,Engineering Applications of Artificial Intelligence, 11:77-85,1998 2. OTHER REFERENCES • A book on Digital Image Processing using MATLAB by Rafael C. Gonzalez, Richard E. Woods and Steven L.Eddins. • Wavelet tutorial By Robi Polikar. 43
    • 44