FINGERPRINT BIOMETRICS Seminar Report

Seminar Report on

Biometrics Based Authentication Using Fingerprint Recognition

Submitted By: SANJEEV MISHRA B.Tech(CSE), 7th Semester Regd. No.-0811012026 ITER, Bhubaneshwar Under the supervision of: Dr.Alok Kumar Jagadev

DECLARATION

I hereby declare that the work presented in this project titled “Biometrics Based Authentication using fingerprint Recognition” submitted by me is an outcome of my own efforts and is an original one.

(SANJEEV MISHRA)

HOD,CSE

SEMINAR GUIDE

FACULTY INCHARGE

ACKNOWLEDGEMENT It is with the greatest pleasure and pride that I present this report before you. First of all, I would like to place myself at the feet of God Almighty for his everlasting love and for the blessings & courage that he gave me, which made it possible to me to see through the turbulence and to set me in the right path. I would like to express my gratitude to all those who gave me the opportunity to complete this project. I want to thank my faculty incharge, Sri S R Das for giving me the permission to commence this seminar in the first instance. I would take this opportunity as a proud privilege to express my deep sense of gratitude to my project guide, Sri Alok Kumar Jagadev.

I would also like to thank my friends who were ready with a positive comment all the time , whether it was an off-hand comment to encourage me or a constructive piece of criticism.

(SANJEEV MISHRA)

DATE: 08/09/2011

ABSTRACT Reliable user authentication is becoming an increasingly important task in the Web-enabled world. The consequences of an insecure authentication system in a corporate or enterprise environment may include loss of confidential information, denial of service, and compromised data integrity. The prevailing techniques of user authentication, which involve the use of either passwords and user IDs (identifiers), or identification cards and PINs (personal identification numbers), suffer from several limitations . Once an intruder acquires the user ID and the password, the intruder has total access to the user's resources. Fortunately, automated biometrics in general, and Fingerprint Technology in particular, can provide a much more accurate and reliable user authentication method. Biometrics is a rapidly advancing field that is concerned with identifying a person based on his or her physiological or behavioral characteristics. Examples of automated biometrics include fingerprint, face, iris, and speech recognition. Because a biometric property is an intrinsic property of an individual, it is difficult to surreptitiously duplicate and nearly impossible to share. The greatest strength of biometrics, the fact that the biometrics does not change over time, is at the same time its greatest liability. Once a set of biometric data has been compromised, it is compromised forever. Fingerprint recognition or fingerprint authentication refers to the automated method of verifying a match between two human fingerprints. Fingerprints are one of many forms of biometrics used to identify individuals and verify their identity. The analysis of fingerprints for matching purposes generally requires the comparison of several features of the print pattern. These include patterns, which are aggregate characteristics of ridges, and minutia points. Matching algorithms are used to compare previously stored templates of fingerprints against candidate fingerprints for authentication purposes. In order to do this either the original image must be directly compared with the candidate image or certain features must be compared.

INDEX

Content

Page

Introduction………………………………………………………………………..

6

Biometrics……………………………………………………

7

Finger print authentication…………………………………...

8

Pattern-based and Minutia-based algorithms………………...

10

Advantages of using Finger Recognition…………………….

11

PATTERNS…………………………………………………..

12

Biometric Finger Scanners……………………………………

13

IMPLEMENTATION…………………………………………

14

Biometric vs. Non-Biometric Fingerprinting………………….

15

Conclusion……………………………………………………..

17

Reference………………………………………………………

18

INTRODUCTION Biometrics (or biometric authentication) consists of methods for uniquely recognizing humans based upon one or more intrinsic, physical or behavioral In traits computer science, in particular, biometrics is used as access control. It is also used to identify individuals in groups that are under surveillance. Biometric characteristics can be divided in two main classes: Physiological(fingerprint, face recognition, iris recognition,etc) and Behavioral (voice, vocal tract,etc). A biometric system can operate in the following two modes. In verification mode the system performs a one-to-one comparison of a captured biometric with a specific template stored in a biometric database in order to verify the individual is the person they claim to be. In Identification mode the system performs a one-to-many comparison against a biometric database in attempt to establish the identity of an unknown individual. Fingerprint recognition identifies people by using the impressions made by the minute ridge formations or patterns found on the fingertips. Finger printing takes an image of a person's fingertips and records its characteristics - whorls, arches, and loops are recorded along with patterns of ridges, furrows, and minutiae. Information is processed as an image and further encoded as a computer algorithm. Fingerprint based biometrics has primarily been used to secure entry devices for building door locks and computer network access. A small number of banks use fingerprint readers for authorization at ATMs. Grocery stores are experimenting with a fingerprint scan checkout that automatically recognizes and bills a registered user's credit card or debit account. More recent applications of finger recognition include use of fingerprints for voter registration

BIOMETRICS Biometrics (or biometric authentication) consists of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. In computer science biometrics is used as a form of identity access management and access control. Biometric characteristics can be divided in two main classes: 1.Physiological (fingerprint, face recognition, iris recognition,etc) 2. Behavioral (voice, vocal tract,etc). Biometrics are unique human feature such as finger prints, hand geometry, face and iris or retinal patterns, DNA and voice. Being the intrinsic properties of an individual, these are difficult to surreptitiously duplicate and nearly impossible to share. Reliable user authentication is becoming an increasingly important task in the Web-enabled world. The value of reliable user authentication is not limited to just computer or network access. Many other applications in everyday life also require user authentication, such as banking, ecommerce, and physical access control to computer resources, and could benefit from enhanced security. India is undertaking an ambitious mega project to provide a unique identification number to each of its 1.25 billion people. The Identification number will be stored in central databases. consisting the biometric information of the individual. This would be the biggest implementation of the Biometrics in the world.

Finger print authentication Fingerprint recognition or fingerprint authentication refers to the automated method of verifying a match between two human fingerprints. Fingerprints are a distinctive feature and remain invariant over the lifetime of a subject, except for cuts and bruises. A fingerprint impression is acquired, typically using an inkless scanner. The digital image of the fingerprint includes several unique features in terms of ridge bifurcations and ridge endings, collectively referred to as minutiae. A fingerprint sensor is an electronic device used to capture a digital image of the fingerprint pattern. The analysis of fingerprints for matching purposes generally requires the comparison of several features of the print pattern. These include patterns, which are aggregate characteristics of ridges, and minutia points, which are unique features found within the patterns. PATTERNS The three basic patterns of fingerprint ridges are the arch, loop, and whorl. An arch is a pattern where the ridges enter from one side of the finger, rise in the center forming an arc, and then exit the other side of the finger. The loop is a pattern where the ridges enter from one side of a finger, form a curve, and tend to exit from the same side they enter. In the whorl pattern, ridges form circularly around a central point on the finger.

Minutia features The major Minutia features of fingerprint ridges are: ridge ending, bifurcation, and short ridge (or dot). The ridge ending is the point at which a ridge terminates. Bifurcations are points at which a single ridge splits into two ridges. Short ridges (or dots) are ridges which are significantly shorter than the average ridge length on the fingerprint. Minutiae and patterns are very important in the analysis of fingerprints since no two fingers have been shown to be identical. Fingerprint Sensors 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 create a biometric template (a collection of extracted features) which is stored and used for matching. Algorithms Matching algorithms are used to compare previously stored templates of fingerprints against candidate fingerprints for authentication purposes. In order to do this either the original image must be directly compared with the candidate image or certain features must be compared.

Pattern-based (or image-based) algorithms Pattern based algorithms compare the basic fingerprint patterns (arch, whorl, and loop) between a previously stored template and a candidate fingerprint. This requires that the images be aligned in the same orientation. To do this, the algorithm finds a central point in the fingerprint image and centers on that. In a pattern-based algorithm, the template contains the type, size, and orientation of patterns within the aligned fingerprint image. The candidate fingerprint image is graphically compared with the template to determine the degree to which they match.

Minutia-based algorithms Minutia based algorithms compare several minutia points (ridge ending, bifurcation, and short ridge) extracted from the original image stored in a template with those extracted from a candidate fingerprint. Similar to the pattern-based algorithm, the minutia-based algorithm must align a fingerprint image before extracting feature points. This alignment must be performed so that there is a frame of reference. It is important to note that an actual image of the print is not stored as a template under this scheme. Before the matching process begins, the candidate image must be aligned with the template coordinates and rotation. Features from the candidate image are then extracted and compared with the information in the template. Depending on the size of the input image, there can be 10-100 minutia points in a template. A successful match typically only requires 7-20 points to match between the two fingerprints.

Advantages of using Finger Recognition

 Fairly small storage space is required for the biometric template, reducing the size of the database required.  It is one of the most developed biometrics.  Each and every fingerprint including all fingers are unique, even identical twins have different fingerprints.  Sound potential for forensic use as most of the countries have existing fingerprint databases.  Relatively inexpensive and offers high levels of accuracy.

PATTERNS

Biometric Finger Scanners Computerized fingerprint scanners have been a mainstay of spy thrillers for decades, but up until recently, they were pretty exotic technology in the real world. It consists of : 1. A reader or scanning device. 2. Software that converts the scanned information into Digital form and compares match points. 3. A Database that stores the Biometric data for comparison.

IMPLEMENTATION Matching algorithms are used to compare previously stored templates of fingerprints against candidate fingerprints for authentication purposes. In order to do this either the original image must be directly compared with the candidate image or certain features must be compared. The next step is to locate these features in the fingerprint image, using an automatic feature extraction algorithm. Each feature is commonly represented by its location (x, y) and the ridge direction at that location (). Pattern based algorithms compare the basic fingerprint patterns (arch, whorl, and loop) between a previously stored template and a candidate fingerprint. This requires that the images be aligned in the same orientation.To do this, the algorithm finds a central point in the fingerprint image and centers on that. The candidate fingerprint image is graphically compared with the template to determine the degree to which they match. In the final stage, the matcher subsystem attempts to arrive at a degree of similarity between the two sets of features after compensating for the rotation, translation, and scale. This similarity is often expressed as a score. Based on this score, a final decision of match or no-match is made. A decision threshold is first selected. If the score is below the threshold, the fingerprints are determined not to match; if the score is above the threshold, a correct match is declared.Often the score is simply a count of the number of the minutiae that are in correspondence.

Biometric vs. Non-Biometric Fingerprinting The aura of criminality that accompanies the term "fingerprint" has not significantly impeded the acceptance of fingerprint technology, because the two authentication methods are very different. Fingerprinting, as the name suggests, is the acquisition and storage of the image of the fingerprint. Fingerprinting was for decades the common ink-and-roll procedure, used when booking suspects or conducting criminal investigations. More advanced optical or non-contact fingerprinting systems (known as live-scan), which normally utilize prints from several fingers, are currently the standard for forensic usage. Many people think of forensic fingerprinting as an ink and paper process. While this may still be done in some locations, most jurisdictions utilize optical scanners known as livescan systems. There are some fundamental differences between these forensic fingerprinting systems (used in AFIS systems) and the biometric fingerprint systems used to logon to a PC: Response time - AFIS systems may take hours to match a candidate, while fingerprint systems respond with seconds or fractions of seconds. Cost - an AFIS capture device can range from several hundred to tens of thousands of dollars, depending on whether it is designed to capture one or multiple fingerprints. A PC peripheral fingerprint device generally costs less than $200) Accuracy - an AFIS system might return the top 5 candidates in a biometric comparison with the intent of locating or questioning the top suspects. Fingerprint systems are designed to return a single yes/no answer based on a single comparison. Scale – AFIS systems are designed to be scalable to thousands and millions of users, conducting constant 1:N searches. Fingerprint systems are almost invariably 1:1, and do not require significant processing power.

Capture – AFIS systems are designed to use the entire fingerprint, rolled from nail to nail, and often capture all ten fingerprints. Fingerprint systems use only the center of the fingerprint, capturing only a small fraction of the overall fingerprint data. Storage – AFIS systems generally store fingerprint images for expert comparison once a possible match has been located. Fingerprint systems, by and large, do not store images, as they are not used for comparison. Infrastructure – AFIS systems normally require a backend infrastructure for storage, matching, and duplicate resolution. These systems can cost hundreds of thousands of dollars. Fingerprint systems rely on a PC or a peripheral device for processing and storage.

CONCLUSION Fingerprint is the cheapest, fastest, most convenient and most reliable way to identify someone. Fingerprint authentication has many usability advantages over traditional systems such as passwords. The greatest strength of Fingerprint recognition, the fact that the Fingerprint does not change over time, is at the same time its greatest liability. Today, fingerprint recognition technology is used for security purposes, to restrict access or to protect computers. As fingerprint recognition technology develops, it is expected that more affordable and more portable fingerprint recognition devices will become available, and finger-print recognition will be considered a safe and convenient personal identification system.Eventually, fingerprint recognition will be used to secure the safety and reliability of a variety of businesses in the industrial sector, including the personal devices and financial industry.

REFERENCES BOOKS: 1. Biometric, by John D. Woodward (Jr.), Nicholas M. Orlans, Peter T. Higgins. 2. Handbook of Fingerprint Recognition, by Davide Maltoni, Dario Maio, Anil K. Jain 3. Pattern Recognition and Image Analysis,by Joan Martí, Jose Miguel Benedi, Ana Maria Mendonça.

WEB:

The links are: 1. WIKIPEDIA-http://en.wikipedia.org/wiki/Fingerprint_recognition. 2.

http://www.biometricsinfo.org/fingerprintrecognition.htm.

3. http://www.springer.com/computer/image+processing/book/978-1-84882253-5 4. http://bias.csr.unibo.it/maltoni/handbook/ 5. http://computer.howstuffworks.com/fingerprint-scanner.htm 6. www.scribd.com.