DOC PREVIEW
SJSU CS 265 - Introduction & Basic Methodology

This preview shows page 1-2 out of 7 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 7 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 7 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 7 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

EXAMPLE OF IMPLEMENTATIONIris RecognitionIntroduction & Basic MethodologyIris recognition may not be on of the wide spread technologies used forauthentication but it has one of the lowest error rates when compared to otherbiometric technologies. Irises patterns are individual and do not change withage. It is also a fact that the iris patterns of the left and right eyes within thesame individual are different from each other. The texture of the iris is madeup of a complex fibrous and elastic tissue sometimes referred to as thetrabecular meshwork. This fine detail of this mesh like structure is establishedprior to birth and remains intact throughout the life of the individual. Iris pattern is of a somewhat regular polar geometry making it easy to developa co-ordinate system for feature recognition. A point to be taken intoconsideration is the fact that the surface of the iris is mobile since the pupilexpands and contracts. The visible portion of the iris differs based on ethnicorigin and genetic inheritance; in individuals with dark eyes the importantquestion is how easily the boundaries between pupil and iris may be identified.The first step would be to capture the image of the iris using a CCD (ChargeCoupled Device) camera. After the image has been captured we use a circularedge detector to identify and locate the boundary between the white portionof the eye (Sclera) and the iris and proceed further to distinguish the boundarybetween the iris and the pupil [Figure 1]. Figure 1 Figure 2 After this we define circular contours of increasing radius so that we havezones of analysis [Figur2], which remain the same irrespective of pupil resizingactivity. Parts of the iris that are hidden by the eyelids/eyelashes, or corruptedby reflections from glasses are detected and masked out so the encoding ofthe iris is not influenced. One must notice that the pupil is not always centralto the iris. Because the constant movement of the iris multiple images areCryptography and Computer Security (CS 265) Page 1 of 7Author: Mabbu, Sathya Swathi (SJSU# 004243721) & Long Vuong (SJSU#003739074)ScleraIrisPupilIris Recognitioncaptured rapidly till a bona fide image is confirmed. The user can observe thisprocess via a reflected image of the eye present in the CCD camera, whichserves as an aid for the user to focus and stabilize the image. Now we analyze the zones of analysis [Figure 2] and distinguish feature withinthese zones, for this purpose we use 2D Gabor filters which basically provideinformation about orientation and spatial frequency of minutiae within theimage sectors. Integro-differential operators of the form given below do thesedetection operations,Max(r, x0, y0) G  (r) *   r, x0,y0 I(x,y) r 2rwhere contour integration is parameterized for size and location coordinates r,x0, y0 at a scale of analysis  set by G  (r) is performed over image data I(x,y).Then a coordinate system is defined which maps the tissue, this coordinatesystem is pseudo polar and compensates automatically for the stretching ofthe iris tissue as the pupil dilates. The detailed pattern is encoded into a 256-byte code by demodulating it with 2D Gabor wavelets, which represent thetexture by phasors in the complex plane. For each element of the iris patternthe phasor angle is mapped to its respective quadrant where it lies. Dr. John Daugman developed the iris-scanning algorithm, which is widely usednowadays. The amazing fact is that the entire process of image capturing,zoning, analysis and iris code creation is typically completed in less than asecond. The current implementations of the iris scanning approach includesome amount of user interaction in order to properly capture the image, but itis basically a non-contact approach. It is found that the iris scanning approachworks well with spectacle and contact lens users. A decision made by a biometric system is generally a ‘genuine’ or ‘imposter’decision, which can be represented using two statistical distributions, genuinedistribution and imposter distribution. For each type of decision there will betwo possible outcomes i.e., true / false. In that case there are four results,listed below1. a genuine person is accepted2. a genuine person is rejectedCryptography and Computer Security (CS 265) Page 2 of 7Author: Mabbu, Sathya Swathi (SJSU# 004243721) & Long Vuong (SJSU#003739074)dsIris Recognition3. an imposter is rejected4. an imposter is acceptedResults 1 and 3 are correct whereas 2 and 4 are incorrect. Now we can definethe performance criteria for this system. So we define the False (imposter)Acceptance Rate (FAR) and False (genuine) Rejection Rate (FRR). In order toprovide a more reliable assessment of the system we can define some morecriteria, the first is the Reliable Operating Curve (ROC) and ‘d’. An ROC givesperformance results (FAR and FRR) for the system at various operating points;‘d’ gives the distance between the genuine distribution and imposterdistribution. In other words ‘d’ measures how well separated the twodistributions are, since recognition errors are caused by their overlap. If their means are 1 and 2 and their standard deviations are 1 and 2, then‘d’ is defined as d = 1 - 2 SQRT [(12 + 22)/2] 222,743 comparisons of different iris patterns yielded a mean value 1=0.089 and 1 =0.042 340 comparisons of sane iris pairs yielded a mean value of 2 =0.456 and 2=0.018The value of d is found to be 11.36 for iris recognition, which is much higherthan that reported for any other biometric system. Till now we have covered the theoretical aspects of the technology behind irisrecognition, now we move on to see a practical implementation. We havetaken the implementation model example of National Instruments (NIDAYS),Italy.EXAMPLE OF IMPLEMENTATIONThe hardware consists of a standard PC with the Microsoft Windows as OS, theNI 1411 acquisition board, and an analogic color single chip CCD camera. Thesystem architecture is structured as shown in Fig.1. Cryptography and Computer Security (CS 265)


View Full Document

SJSU CS 265 - Introduction & Basic Methodology

Documents in this Course
Stem

Stem

9 pages

WinZip

WinZip

6 pages

Rsync

Rsync

7 pages

Hunter

Hunter

11 pages

SSH

SSH

16 pages

RSA

RSA

7 pages

Akenti

Akenti

17 pages

Blunders

Blunders

51 pages

Captcha

Captcha

6 pages

Radius

Radius

8 pages

Firewall

Firewall

10 pages

SAP

SAP

6 pages

SECURITY

SECURITY

19 pages

Rsync

Rsync

18 pages

MDSD

MDSD

9 pages

honeypots

honeypots

15 pages

VPN

VPN

6 pages

Wang

Wang

18 pages

TKIP

TKIP

6 pages

ESP

ESP

6 pages

Dai

Dai

5 pages

Load more
Download Introduction & Basic Methodology
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view Introduction & Basic Methodology and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Introduction & Basic Methodology 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?