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THE DESIGN AND IMPLEMENTATION OF ID AUTHENTICATION SYSTEM BASED ON

FINGERPRINT AND IRIS IDENTIFICATION

THUNUGUNTLA RAJYALAKSHMI1,K.KOTESWARARAO2, K.ANURADHA3 1M.Tech Student, Dept of ECE, Prakasam Engineering College, Prakasam Dist, A.P, India

2Assistant Professor, Dept of ECE, Prakasam Engineering College, Prakasam Dist, A.P, India 3Assistant Professor, Dept of MCA, Yalamarty Institute Of Computer Science,Visakapatnam, A.P, India

Abstract–– This paper explores the concept of Iris recognition which is one of the most popular biometric techniques. Initially the users need to enroll their finger prints and eye images that will be saved in the data base. In next step the user is made to show his RFID tag to the reader. If it is invalid card it gives the error message displaying “invalid person” and again it displays to show your RFID card. If the card is a valid one then user had to enter the password, if user enters a wrong password it will not move to the next step and buzzer will be ON. If the password accessing is continuously failed for three times then process will move to the initial condition i.e. rfid tag showing step. If the user enters the correct password then controller asks for a fingerprint access. If the finger print access is failed then buzzer will be on and the process will move to the first step i.e. RFID tag showing step. If fingerprint access is matched with stored fingerprint or authorized person finger print then controller asks for Iris reorganization. i.e., the person who ever wants to access his/her particular accessories or things first he/she have to place his eye in front of the PC camera so that it will capture the image of eye and compares with previous eye image. If captured image does not match with first taken image then the controller gives a halt to the process and moves to the initial step RFID tag showing step. Keywords-Biometrics, IRIS, ATM, Segmentation, Feature extraction, IRIS localization, Normalization.

I. INTRODUCTION Biometric authentication technique based on iris patterns is suitable for high level security systems. Iris scan biometrics employs the unique characteristics and features of the human iris in order to verify the identity of an individual. IRIS is the area of the eye where the pigmented or colored circle, usually brown or blue, rings the dark pupil of the eye. The variations in the gray level intensity values distinguish two individuals. The difference exists

between the identical twins and even between left and right eye of the same person. As the technology is iris pattern-dependent, not sight dependent. Biometric technologies have been proposed to strengthen authentication mechanisms in general by matching a stored biometric template to a live biometric template. The iris-scan process begins with a photograph. A specialized camera, typically very close to the subject, no more than three feet, uses an infrared imager to illuminate the eye and capture a very high-resolution photograph. This process takes only one to two seconds and provides the details of the iris that are mapped, recorded and stored for future matching/verification. Eyeglasses and contact lenses present do not create any problems to the quality of the image. Iris-scan systems test for a live eye by checking for the normal continuous fluctuation in pupil size. The inner edge of the iris is located by an iris-scan algorithm which maps the iris’ distinct patterns and characteristics. An algorithm is a series of directives that tell a biometric system how to interpret a specific problem. Algorithms have a number of steps and are used by the biometric system to determine if a biometric sample and record is a match. Iris’ of a human are composed before the birth and, except in the event of an injury to the eyeball, remain unchanged throughout an individual’s lifetime. Iris patterns are extremely complex, carry an astonishing amount of information and have over 200 unique spots. The fact that an individual’s right and left eyes are different and that patterns are easy to capture, establishes iris-scan technology as one of the biometrics that is very resistant to false matching and fraud. The false acceptance rate for iris recognition systems is 1 in 1.2 million, statistically better than the average fingerprint recognition system. The real benefit is in the false-rejection rate, a measure of authenticated users who are rejected. Fingerprint scanners have a 3 percent false-rejection rate, whereas iris scanning

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systems boast rates at the 0 percent level. A highly accurate technology such as iris-scan has vast appeal because the inherent argument for any biometric is, of course, increased security. EXISTING SYSTEM In addition, authentication also serves as the first step for many other security purposes, such as key management and secure group communication. Passwords or smartcards have been the most widely used authentication methods due to easy implementation and replacement; however, memorizing a password or carrying a smartcard, or managing multiple passwords/smartcards for different systems (one for each system),is a significant overhead to users. DISADVANTAGES OF EXISTING SYSTEM If the person’s ID card and the password are stolen by his colleagues or family members then the things will be stolen in the existing system. By using authorized person’s identity card some other person will enter in that particular authorized areas or restricted area in military.

II. PROPOSED SYSTEM STRUCTURE AND PROTOTYPE DESIGN

Figure 1: Block Diagram of the Project

A. Analysis of hardware Structure

1) ARM7TDMI: ARM architecture is based on Reduced Instruction Set Computer (RISC) Principles. The RISC instruction set, and related decode mechanism are much simpler than those of Complex Instruction Set Computer (CISC) designs. This simplicity gives:

• A high instruction throughput

• An excellent real-time interrupt response

• A small, cost-effective, processor macro cell.

Microcontroller: A Micro controller consists of a powerful CPU tightly coupled with memory RAM, ROM or EPROM), various I / O features such as Serial ports, Parallel Ports, Timer/Counters, Interrupt Controller, Data Acquisition interfaces-Analog to Digital Converter (ADC), Digital to Analog Converter (ADC), everything integrated onto a single Silicon Chip.

2) RFID reader Module: This is used to automatically identify the products tagged within the communication range of the reader, which will be able to provide the accurate consignments and real-time automatically manifest, and improve movable asset management accuracy and efficiency. 3) Keys Section: With the help of these keys the users can enroll their eye images and will enter their passwords.

4) PC Cam Section: This section is basically meant to capture the IRIS of the persons and to transfer this captured IRIS for Processing.

5) IRIS: In this we are using the Iris recognition technique. Iris recognition analyses the features that exist in the colored tissue surrounding the pupil, which has 250 points used for comparison, including rings, furrows, and freckles. Iris recognition uses a regular video camera system and can be done from further away than a retinal scan. It has the ability to create an accurate enough measurement that can be used for Identification purposes, not just verification.

6) Buzzer: This is the output device which we are using to indicate the unauthorized person.

7) ATM SYSTEM: Here we can check the transactions like balance enquiry, money with-drawl, statements etc.

8) Finger print: A fingerprint sensor is an electronic device used to capture a digital image of the fingerprint pattern. The captured image is called a live scan. This live scan is digitally processed to

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create a biometric template (a collection of extracted features) which is stored and used for matching.

B. Building the Prototype System

Initially the users will enroll their eye images that will be saved in the data base. Figure1 shows the block diagram of the project.

Step1: The person who ever wants to access his ATM, first of all he has to show his id card in front of the card accessing machine i.e. reader. If it is a valid one then it goes to second step. Otherwise Buzzer will be on and it gives the error message display like invalid person again it displays to show your RFID card.

Step2: In this step the user have to enter the correct password, if the user enters the wrong password it will not move to the next step and the buzzer will be on, if the password accessing is continuously failed for three times then process will move to the initial condition i.e. RFID tag showing step. If the user enters the correct password then controller asks for the next step (Finger print).

Step3: If the user enters the correct password then controller asks for a fingerprint access. If the finger print access is failed then buzzer will be on and the process will move to the first step i.e. RFID tag showing step. If fingerprint access is matched with stored fingerprint or authorized person finger print then controller asks for Iris reorganization.

Step4: In this step the person who ever wants to access his ATM first he/she has to place his eye in front of the PC camera at that time it will capture the image of eye and compares with previous eye image in that same way we can access some others with matching or compare of eye image and this whole process will be done with the help of matlab code. If it matches the ATM machine will ask for the further transactions like balance enquiry, money with-drawl etc and the process goes on. If captured image does not match with the image stored in the database then the controller gives a halt to the process and moves to the initial step1 at the same time buzzer will be on. Figure2 shows the complete system operation flow.

III. System operation flow

In this project initially the users will enroll their eye images that will be saved in the data base. In this project initially the user is made to show a RFID tag to the reader. If it is a valid one then the user has to enter the password, if user enters wrong password the buzzer will be on, if the password accessing is

continuously failed for three times means then process will move to the initial condition i.e. RFID tag showing step.

Figure 2: The system operation flow

If the user enters correct password then controller asks for the iris identification access, In the IRIS identification the person who ever want access the ATM machine he/she has to place his eye in front of the PC camera at that time it will capture the image of eye and comparing with previous eye image in that same way we can access some others with matching or compare of eye image and this whole process will

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done with the help of matlab code. If it matches then the user can have options for further transactions like money with- drawl, balance enquiry, mini statement online transactions etc.

IV.BENEFITS OF USING IRIS TECHNOLOGY

The iris is a thin membrane on the interior

of the eyeball. Iris patterns are extremely complex. Patterns are individual (even in fraternal or identical twins).Patterns are formed by six months after birth, stable after a year. They remain the same for lifetime. Imitation is almost impossible. Patterns are easy to capture and encode. Biometrics is the automated recognition of individuals based on behavioral and biological characteristics. The technology is designed to automatically take a picture from person and match it to the digitized image stored in the biometric passport. In the field of financial services, biometric technology has shown a great potential in offering more comfort to customers while increasing their security.

Applications due to information protection issues, it is believed that the technology will find its way to be widely used in many different applications. Biometrics such as signatures, photographs, fingerprints, voiceprints, DNA and retinal blood vessel patterns all have significant drawbacks. Face Recognition: Changes with Age, Expression, Viewing angle, Illumination. Finger Print Recognition: Fingerprints or handprints require physical contact, and they also can be counterfeited and marred by artifacts. IRIS recognition is one among the biometric systems the tool used for this recognition is MATLAB. To determine the uniqueness of iris patterns in terms of hamming distance distribution by comparing template generated from different eyes. 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 culinary zone and an inner papillary zone, and these two zones are divided by the collarets which appears as a zigzag pattern. Success rate: Failure rate using IRIS technology is just 1 in 1.2 million. When compared to other technology systems this was found as very efficient. Hence we have chosen this technology. V. ARCHITECTURE OF IRIS RECOGNITION

A complete iris recognition system can be split into four stages: Image acquisition, segmentation, encoding and matching. The data acquisition step captures the iris images. Infra-red illumination is used in most iris image acquisition. The iris segmentation step localizes the iris region in the image. For most algorithms, and assuming near-frontal presentation of the pupil, the iris boundaries are modeled as two circles, which are not necessarily concentric. The inner circle is the papillary boundary (between the pupil and the iris). The outer circle is the limbic boundary (between the iris and the sclera). The noise processing is often included in the segmentation stage. The encoding stage encodes the iris image texture into a bit vector code. In most algorithms, filters are utilized to obtain information about the iris texture. Then the outputs of the filters are encoded into a bit vector code. The corresponding matching stage calculates the distance between iris codes, and decides whether it is an authorized match or unauthorized match. Enrollment: The enrollment phase creates a user profile for subsequent authentication activities. Typically, a new user provides multiple biometric reading samples that are combined to form one stored record. Authentication: Where a template is created for an individual and then a match is searched for in the database of pre-enrolled template.

Fig3 Architecture of iris recognition system

Image acquisition: One of the major challenges of automated iris recognition is to capture a high-quality image of the iris while remaining non-invasive to the

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human operator. 3.4 Image Segmentation: At this stage, the iris is extracted from the eye image. The extracted iris region was then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. The integro-differential operator for locating the circular iris and pupil regions, and also the arcs of the upper and lower eye lids. The integro-differential operator is defined as

푚푎푥 , , |퐺 ∗푑푑푥 (푟,푥 ,푦 )

퐼(푥,푦)2휋푟 푑푠|

Where I(x,y) is the eye image, r is the radius to search for, Gs(r) is a Gaussian smoothing function, and s is the contour of the circle given by r, xo, yo, The operator searches for the circular path where there is maximum change in pixel values, by varying the radius and centre x and y position of the circular contour. The operator is applied iteratively with the amount of smoothing progressively reduced in order to attain precise localization. Eyelids are localized in a similar manner, with the path of contour integration changed from circular to an arc. The segmented iris image is normalized and converted from Cartesian image coordinates to polar image coordinates.

3.5. Feature Extraction: The iris contains important unique features, such as stripes, freckles, coronas, etc. These features are collectively referred to as the texture of the iris. The 2D version of Gabor filters in order to encode iris pattern data. A 2D Gabor filter over the an image domain (x, y) is represented as

퐺( , )

= 푒[

( )

( )] 푒 [ ( ) ( )]

Where (x0, y0) specify position in the image, (α, β) specify the effective width and length, and (u0,v0) specify modulation, which has spatial frequency the odd symmetric and even symmetric 2D Gabor filters are shown in figure. Daugman demodulates the output of the Gabor filters in order to compress the data. This is done by quantizing the phase information into four levels, for each possible quadrant in the complex plane. 3.6 Matching: This phase consists of two steps, namely matching and identification. In the matching process, the extracted features of the iris are compared with the iris images in the database. If enough similarity is found, the subject is then identified. 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.

Fig 4 Flow chart for IRIS recognition technique 3.7 Hamming Distance: Hamming distance was originally conceived for detection and correction of errors in digital communication. It is simply defined as the number of bits that are different between two bit vectors. 퐻퐷 = ∑ ( ) VI. CONCLUSION The technical performance capability of the iris recognition process far efficient than that of any biometric technology now available today. Biometric technology has now become a viable alternative to traditional identification systems because of its tremendous accuracy and speed. As iris technology grows less expensive, it could very likely unseat a large portion of the biometric industry. Its technological superiority has already allowed it to make significant inroads into identification and security venues which had been dominated by other biometrics. Iris-based biometric technology has

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always been an exceptionally accurate one, and it may soon grow much more prominent.

Finally, in this section gives features of the iris implementation encoded by convolving the normalized iris region with 1D Log-Gabor filters and phase quantizing the output in order to produce a bit-wise biometric template. The Hamming distance was chosen as a matching metric. The experimental results show that the proposed approach has a faster operation and good recognition performance. All experimental results have demonstrated that the proposed algorithm has an encouraging performance. This also confirms that effective iris segmentation is important for an iris recognition system. However, efforts remain to be taken to further improve its performance. In order to improve the automatic segmentation algorithm, a more elaborate eyelid and eyelash detection system could be implemented. Thus the above method can be efficiently implemented for the ATM transactions.

VII. REFERENCES [1]. J. Daugman. High confidence visual recognition of persons by a test of statistica independence. IEEE Trans.PAMI, 15(11):1148–1161, November 1993. [2]. J. Daugman. The importance of being random: Statistical principles of iris recognition. Pattern Recognition, 36(2):279–291, 2003. [3]. J. Daugman. Anatomy and physiology of the iris. [htlm-doc.], [retrieved 15.10.2003]. From: http://www.cl.cam.ac.uk/users/jgd1000/anatomy.html. [4] R.Wildes. Iris recognition: An emerging biometric technology. Proceedings of the IEEE, 85(9):1348–1363, September 1997. [5]. W. Boles and B. Boashash. A human identification technique using images of the iris and wavelet transform. IEEE Trans. Signal Processing, 46(4):1185 1188, April 1998. [6]. S. Lim, K. Lee, O. Byeon, and T. Kim. Efficient iris recognition through improvement of feature vector and classifier. ETRI Journal, 23(2):61–70, 2001. [7]. S. Noh, K. Pae, C. Lee, and J. Kim. Multiresolution independent component analysis for

iris identification. In Proceedings of ITC-CSCC’02, pages 1674-1678, 2002. [8]. C. Tisse, L.Martin, L. Torres, and M. Robert. Person identification technique using human iris recognition. In Proceedings of ICVI’02, pages 294–299, 2002. [9]. L. Ma, T. Tan, Y. Wang, D. Zhang. Personal Recognition Based on Iris Texture Analysis. IEEE Trans.PAMI, 25(12):1519–1533, 2003. [10]http://fingerprint.nist.gov/latent/elft07/phase1_aggregat.pdf.

[11] J. Feng. Combining minutiae descriptors for fingerprint matching. Pattern Recognition, 41(1):342–352, 2008.

[12] Evaluation of latent fingerprint technologies 2007. http://fingerprint.nist.gov/latent/elft07/.

[13] Conclusion of circuit court judge Susan Souder - grants motion to exclude testimony of forensic fingerprint examiner capital murder case: State of Maryland v. Bryan Rose, October 2007. http://www.clpex.com/Information/STATEOFMARYLAND-v-BryanRose.doc

Thunuguntla Rajyalakshmi, pursuing her M.Tech in Embedded Systems from Prakasam Engineering College, Kandukur mandal, Prakasam Dist, A.P, India. Affiliated to Jawaharlal Nehru Technological University, Kakinada, and is approved by AICTE Delhi.

K. Koteswarrao, his Qualification is M.tech, currently working as an Associate Professor, in the Department of Electronics and communication Engineering, Prakasam Engineering College, Kandukur mandal, Prakasam Dist, A.P, and India. Affiliated to Jawaharlal Nehru Technological University, Kakinada, and is approved by AICTE Delhi.

K.Anuradha, his Qualification is M.tech, currently working as an Associate Professor, in the Department of MCA,Yalamarty college of computer science,Visakapatnam, A.P, and India. Affiliated to Andra University,Visakapatnam, and is approved by AICTE Delhi.