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ABSTRACT

Identification of individuals is a very basic societal requirement. Electronic verification of a personâ„¢s identity is of great importance as more interactions take place electronically. Biometric fingerprint identification is a technique used to change the physical attribute of a person ie; his finger print into electronic processes. Until recently electronic verification was based on something the person had in their possession like a password. But the problem is that these forms arenâ„¢t very secure because passwords can be forgotten or taken away. In biometric finger print identification a scanner is used to get the image of the finger. When a user places his or her finger on the terminals of scanner the image is electronically read, analysed and compared with a previously recorded image of the same finger, which has been stored in finger scan data base. Finger print is a proven technology capable of high levels of accuracy.


1. INTRODUCTION
Positive identification of individuals is a very basic societal requirement. 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 can be catastrophic, and may include loss of confidential information, denial of service, and compromised data integrity. The value of reliable user authentication is not limited to just computer or network access. Many other applications in every day life also require user authentication, such as banking, e-commerce, and could benefit from enhanced security.

In fact, as more interactions take electronically, it becomes even more important to have an electronic verification of a personâ„¢s identity. Until recently, electronic verification took one of two forms. It was based on something the person had in their possession, like a magnetic swipe card, or something they knew, like a password. The problem is, these forms of electronic identification are not very secure, because they can be given away, taken away, or lost and motivated people have found ways to forge or circumvent these credentials.

The ultimate form of electronic verification of a personâ„¢s is biometrics. Biometrics refers to the automatic identification of a person based on his/her physiological or behavioral characteristics such as finger scan, retina, iris, voice scan, signature scan etc. By using this technique physiological characteristics of a person can be changed into electronic processes that are inexpensive and easy to use. People have always used the brainâ„¢s innate ability to recognize a familiar face and it has long been known that a personâ„¢s fingerprints can be used for identification.

2. IDENTIFICATION AND VERIFICATION SYSTEMS
A personâ„¢s identity can be resolved in two ways: identification and verification. The former involves identifying a person from all biometric measurements collected in a database and this involves a one-to-many match also referred to as Ëœcold searchâ„¢. Do I know who you are? is the inherent question this process seeks to answer. Verification involves authenticating a personâ„¢s claimed identity from his or her previously enrolled pattern and this involves a one to one match. The question it seeks to answer is, Are you claim to be?

2.1 VERIFICATION
Verification involves comparing a personâ„¢s fingerprint to one that pass previously recorded in the system database. The person claiming an identity provided a fingerprint, typically by placing on a capacitance scanner or an optical scanner. The computer locates the previous fingerprint by looking at the personâ„¢s identity. This process is relatively easy because the computer needs to compare two fingerprint records. The verification process is referred as a Ëœclosed searchâ„¢ because the search field is limited. The second question is who is this person? This is the identification function, which is used to prevent duplicate application or enrollment. In this case a newly supplied fingerprint is supplied to all others in the database. A match indicates that the person has already enrolled/applied.

2.2 IDENTIFICATION
The identification process, also known as an Ëœopen searchâ„¢, is much more technically demanding. It involves many more comparisons and may require differentiating among several database fingerprints that are similar to the objects.

3. BIOMETRIC SYSTEMS AND DEVICES
A biometric system is a combined hardware/software system for biometric identification or verification. Main functions of a biometric system are as follows:

¢ Receive biometric samples from an enroller or candidate.
¢ Extract biometric feature from the sample.
¢ Compare the sample of the candidate with stored templates from individuals.
¢ Indicate identification or verification upon the result of the previous comparison.

Biometric devices have three primary components:
¢ One is an automated mechanism that scans and captures a digital or analog image of a living characteristic.
¢ The second handles comparison of the image with the stored data.
¢ The third interfaces with application systems.

These pieces may be configured to suit different situations. A common issue is where the stored images reside; on a card presented by the person being verified or at host computer. Recognition occurs when an individualâ„¢s is matched with one of a group of stored images.



4. BIOMETRIC ACCURACY

Biometric accuracy is the systemâ„¢s ability of separating legitimate matches from imposters. There are two important performance characteristics for biometric systems.

¢ False rejection is the situation when a biometric system is not able to verify the legitimate claimed identity of an enrolled person.
¢ False acceptance is a situation when a biometric system wrongly verified the identity by comparing biometric features from not identical individuals.
¢ False Rejection Rate (FRR) refers to the statistical probability that the biometric system is not able to verify the legitimate claimed identity of an enrolled person, or fails to identify an enrolled person.
¢ False Acceptance Rate (FAR) refers to the statistical probability of false Acceptance or incorrect verification. In the most common context, both False Rejection and False Acceptance represent a security hazard.


5. FINGERPRINT VERIFICATION
Fingerprinting is probably the best-known biometric- method of identification used for 100 years. Advances in computer technology and communication networks have made even huge fingerprint databases available for instant searches.

Among all the biometric techniques, fingerprint-based Identification is the oldest method that has been successfully used in numerous applications. Everyone is known to have unique, immutable fingerprints. A fingerprint is made of a series of ridges and furrows on the surface of the finger. The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as minutiae points. Minutiae points are local ridge characteristics that occur at either a ridge bifurcation or a ridge ending.

There are a variety of approaches to fingerprint verification. Some try to emulate the traditional police method of matching minutiae, others are straight pattern matching devices, and some adopt a unique approach all of their own, including thermal properties and ultrasonic. Finger-scan technology is the leading biometric authentication technology in use today with the greatest variety of fingerprint devices presently available. This is partially due to the historical use of the fingerprint in law enforcement as well as the fact that the technology lends itself to a more affordable solution.

VIEW OF A FINGER PRINT
6. FINGERSCAN
Fingerscan is an authentication terminal which verifies a persons identity from their finger image. When a user places their finger on the terminals scanner the image is electronically read, analysed, and compared with a previously recorded image of the same finger which has been stored in the fingerscan database. Users call up their finger image by keying in an identification number. This ID number does not need to be classified as it is not part of the security system it simply retrieves the image that will be compared to the users finger scan.

Fingerscan contains its own database of finger images (called templates), user privileges and authorities, and maintains a log of every transaction and message which it records. The system can be accessed through a laptop, networked to a PC, or connected via a modem to a remote host computer.

6.1 THE TECHNOLOGY BEHIND FINGERSCAN
Fingerscan is a biometrics product which involves using some unique biological characteristic or physical property of an individual to verify that persons claimed identity. Biometrics-based identification replaces systems which rely on something a person has in their possession, such as a key or ID card, or something a person knows, such as a password or privileged information. The imaging process is based on digital holography, using an electro-optical scanner about the size of a thumbprint. The scanner reads three-dimensional data from the finger such as skin undulations, and ridges and valleys, to create a unique pattern that is composed into a template file and recorded in the fingerscan database.

The pattern is not a fingerprint and a fingerprint cannot in any way be created from the template. A template can only be compared with a newly presented live finger image and not with other templates. One reason for this is that the data capture process used to create a template is random. If two templates were created one after another for the same finger, each template would be different. This eliminates the possibility of database matching and enhances users privacy.

6.2 THE ALGORITHMS
Fingerprint classification can be viewed as a coarse level matching of the fingerprints. As input fingerprint is matched at a coarse level to one of the prespecified types and then, at a finer level, it is compared to the subset of the database containing that type of fingerprints only.

An algorithm is developed to classify fingerprints into five classes, namely, whorl, right loop, arch and tented arch. The algorithm separates the number of ridges present in four directions (o degree, 45 degree, 90 degree and 135 degree) by filtering the central part of a fingerprint with a bank of Gabor filters. This information is quantized to generate a finger code which is used for classification. More recently, it has become possible to scan a personâ„¢s fingerprint into virtual storage in a computer with the aid of laser technology. In order to prove identification, a personâ„¢s fingerprint will be scanned again in the future by a similar device, and a match of print to name is verified through information system.



6.3 SYSTEM FUNCTIONS
The major FINGERSCAN functions are:
? Enrolment
? Verification
? Time Zones
? Door access
? Template management

¢ Enrolment
Enrolment is the process of scanning a finger to create an image which is stored as a template. Each time the user places his or her finger on the scanner the image is compared to the one represented by the template to verify their identity.

A user with enrolment authority carries out enrolment at designated fingerscan units. The process takes approximately 25 seconds and the resultant template may be stored in various places: in the unit itself, on a personal computer, in a mainframe computer, on a smart card, and so on.

Each user enrolled is allocated a unique ID number, which they use to call up their template before scanning their finger. No ID number is required where the template is stored on a smart card. Up to three fingers can be enrolled against the same ID number to provide users with more than one verification option. Ideally, one finger on each hand should be enrolled so that if the user injures the finger they usually use for verification an alternate image is available.

This feature also provides for multi-person control, for example, if verification from two users is required to open a safe. In this situation fingerscan can be programmed to require up to four fingers with different ID numbers to be verified before access is granted.

¢ Verification
Verification is carried out when a user either enters their ID number, or inserts their smart card in a smart card reader, and then immediately places their finger on the reader platen. Verification takes about .5 of a second.

Verification for individual users can be set at various threshold levels to account for users who may have very fine, worn, or damaged fingers. In this event reducing their verification threshold can enhance the ease of use.

The overall system verification threshold can be lowered in situations where little or no security is required, for example, time and attendance applications. In this situation it may be more acceptable to give a false acceptance than a false rejection.

¢ Time zones
Up to thirty global or individual time zones can be defined in fingerscan. Each user can have up to two active time zones at any time. Users are allocated a default time zone at enrolment, which can be changed by the system supervisor or from the host computer.


¢ Door Access
A door access list defines which users have access to the facilities controlled by the fingerscan unit. The list can be used in conjunction with time zones to restrict access at certain times. The host computer system can control and manage the door access list and the distribution of templates to each fingerscan unit.

¢ Template Management
Templates can be stored in the fingerscan unit, and/or a host computer, and/or a smart card. Each fingerscan unit has 512Kbytes of non-volatile memory which stores up to 300 templates. The memory can be expanded to 1.5Mbytes which will store more than 1100 templates. Templates are stored with a last used date status. If the memory becomes full, the last used templates will be held locally in the fingerscan unit and the main template database will be held in the host computer. The host will transmit templates to individual units if the requested template is not found locally.

Templates can be deleted by user with Manager or Supervisor status either from the host computer or locally at each fingerscan unit. Templates can be exchanged between a fingerscan unit and the host computer over fixed communications or modem links, or locally to and from a laptop. A template created by the fingerscan unit can be used on any other unit when loaded.

6.4 MANAGEMENT CONTROL
Fingerscan has four levels of management control:
? User
A user submits a finger for verification after entering an ID number
? Enroller
An enroller has user status and can also enrol users onto the system.
? Supervisor
A supervisor has enroller status and can also perform initial system set up procedures, set time zones, set alarm codes, and add and delete templates.
? Manager
A manager has supervisor status and can also perform a total system reset, and disable the supervisors ability to change the setup.
? Transaction Log
A transaction log records every use of a fingerscan unit, the time it was used, and the result. The log will hold at least the last 1000 transactions and will wrap around when it becomes full. The transaction log cannot be erased except on a total system reset by a user with Manager authority. Each transaction is allocated a consecutive audit number that does not wrap around. The number will only be reset to 1 on a total system reset.

6.5 SECURITY
Fingerscan provides an audit trail of the date and time a user accessed the unit, the reason for access, and the result. With a 0.0001% probability of a false acceptance fingerscan provides a level of security which cannot be achieved by any knowledge or token based system.

? Template security
Before a user can do any action on a template such as enrol, delete, or transfer, they must first have their identity verified by FINGERSCAN in the usual way. In doing this, a record is added to the transaction log. Only users with Supervisor or Manager authority levels can access the template database.

? Software Security Control
A password option in the communications setup secures the data flow to a host computer. When each fingerscan unit is initialised by the remote host, the host will generate and download to the unit a unique Computer Generated Access Code (CGAC) of at least six digits. For all subsequent communications the host will check the CGAC before starting the session and then change the CGAC immediately prior to logging off.
The CGAC can always be overridden by a Manager or Supervisor finger verification. This is only likely to be required if the fingerscan unit is being accessed via a laptop PC.

? Hardware security control
The processor board in the processor unit is located inside a metal box which can be fitted with a tamper alarm if required. The processor unit should always be located inside the secure area in locations where fingerscan is providing access or other security control. Fingerscan controls the activation of electric locks or strikes from the processor board so the unit cannot be hot-wired from outside.

? Alarms Control
¢ Send an alarm directly to a monitoring company, dialer, modem, siren, and so on, and allow authenticated users to cancel and reset zone alarms and activate and deactivate building services such as air conditioning and lighting.
¢ Record alarms in the fingerscan transaction log.
¢ Support a request to exit (REX) verification which allows users to open a door from the inside. This can be used to monitor door forced alarms.
? Door Lock Control
Fingerscan can directly control a door lock strike after verification of a user.

? Real Time Clock
Fingerscans real time clock is protected by a lithium battery, and features a day-of-week register and leap year correction.

6.6 AN OVERVIEW OF FINGERSCAN TECHNOLOGIES
The fundamental limiting factor for Finger-scan technology has been the process by which the devices capture an image of the finger. The most common technologies are: Optical, Silicon, Ultrasound and Touchless. Optical Scanner relies on an image of ridges and valleys of the print. The process, referred to as Frustrated Total Internal Reflection, a form of spectroscopy, essentially takes a picture of finger. Silicon or Capacitance Fingerprint scanners often great potential because if utilizes higher image quality than optical surface contamination found on the finger. Thermal Fingerprint scanners uses infrared to sense the temperature differences between the ridges and valleys of the finger to create a fingerprint image. Ultrasonic Fingerprint scanner scans the finger ultrasonically, using high frequency sound waves, to capture an image of the finger.

6.7 CAPACITANCE SCANNER
Capacitive fingerprint scanners generate an image of the ridges and furrows that make up a fingerprint. This type of scanner senses the print using electric current.

CAPACITANCE SCANNER

The diagram shown a simple capacitive sensor, The sensor is made up of one or more semiconductor chips containing an array of tiny cells. Each cell includes two conductor plates, covered with an insulating layer. The cells are tiny “ smaller than the width of one ridge on a finger.

The sensor is connected to an integrator, an electric circuit built around an inverting operation amplifier. The inverting amplifier is a complex semiconductor device, made of a number of transistors, resistors and capacitors.

Like any amplifier “ an inverting amplifier alters one current based on flucturations in another current. Specifically, the inverting amplifier has the inverting terminal and the non/inverting terminal. In this case the non-inverting terminal is connected to ground, and the inverting terminal is connected to a reference voltage supply and a feed back loop. The feed back loop, which is also connected to the amplifier output, includes the two conductor plates.
The two conductor plates form a basic capacitor, an electric component that can store up charge. The surface of the finger acts as a third capacitor plate, separated by the insulating layers in the cell structure and, in the case of the fingerprint valleys, a pocket of air. Varying the distance between the capacitor plates (by mainly the finger closer or farther away from the conducting plates) changes the total capacitance (ability to store charge) of the capacitor. Because of this quality, the capacitor in a cell under a ridge will have a greater capacitance than the capacitor in a cell under a valley.

To scan the finger, the processor first closes the reset switch for each cell, which shorts each amplifier input and output to balance the integrator circuit. When the switch is opened again, and the processor applies a fixed charge to the integrator circuit, the capacitors charge up. The capacitance of the feedback loopâ„¢s capacitor affects the voltage at the amplifierâ„¢s input, which affects the amplifierâ„¢s output. Since the distance to the finger alters capacitance, a finger ridge will result in a different voltage output than a finger valley.

The scanner processor reads this voltage output and determines whether it is characteristic of a ridge or an valley. By reading very cell in the sensor array, the processor can put together an overall picture of the fingerprint, similar to the image captured by an optical scanner.

The main advantage of a capacitive scanner is that it requires a real fingerprint “ type shape rather than the pattern of light and dark that make up the visual impression of a fingerprint. This makes the system harder to trick. Additionally since they use a semiconductor chip rather than a CCD (charge coupled device) unit as in case of Optical scanner, capacitive scanners tend to be more compact than Optical devices.

6.8 ADVANTAGES OF FINGERPRINT SCANNERS
Compared to the other biometric authentication technologies, fingerprint scanners are:
? The most widely available device.
? Relatively low cost
? Small size (easily integrated into keyboards) and
? Easy to integrate

Fingerprint verification may be a good choice for in-house systems where adequate explanation and training can be provided to users and where the system is operated within a controlled environment.

6.9 DISADVANTAGES
Fingerprint verification can suffer under large-scale usage. In a large population, poorly trained users cause higher usage errors and hence higher instances of false rejection. Also, the user interface (scanning module) can become damaged or dirty by large-scale usage.


7. FUTURE APPLICATIONS
There are many concerning potential fingerprint applications, some popular examples being:

7.1 ATM MACHINE USE
Most of the leading banks have been experimenting with biometrics of ATM Machines use and as general means of combining card fraud. It is estimated that lesser due to identity fraud in welfare disbursements, credit card transactions, cellular telephone calls, and ATM withdrawals total over $ 6 billion every year. At present an ATM identifier a person as a client after the person inserts an ATM card into the machine and enters a personal identification number (PIN). This method of identification has its drawbacks. According to researchers, about one-fourth of bank customers apparently write their PIN on their ATM card, thus defeating the protection offered by a PIN when an ATM card is stolen.

7.2 INTERNET TRANSACTIONS
Security for information systems and computer networks is another important area for fingerprint applications. Access to databases by means of remote login is another application. Some experts anticipate that more and more information systems, computer networks, and world wide web sites will use fingerprint identification techniques to control access and for other security purposes.



7.3 PERSONAL TRANSPORTATION
Several leading automobile manufacturers are exploring the use for fingerprint identification to enable an authorized driver to enter and start a car without using a key.

7.4 USE IN PUBLIC SECTOR
Various government agencies have considered using biometric fingerprint identification. In benefits distribution programs such as welfare disbursement, fingerprint identification techniques could bring about substantial savings by deterring the same person from filing multiple claims. Fingerprint based voter registration can be used to verify identity at the polls to prevent fraudulent voting. In Academics/certifications it can be used to verify personâ„¢s identity prior to taking an exam.


8. CONCLUSION
Biometric fingerprint identification has many usability advantages over traditional systems such as passwords. Specifically, users can never lose their fingerprints, and the fingerprint is difficult to steal or forge. The intrinsic bit strength of a fingerprint is quite good when compared to conventional passwords. Finger scanners are getting smaller, cheaper, and more accurate, and can be used in mobile gadgets without sprucing up the size, cost, and power consumption. By using this technology theft can be prevented and can also eliminate fraudulent transactions. Mobile manufacturers and wireless operators are incorporating voice and fingerprint scanning techniques in their devices. Fingerprint is a very strong desktop solution, and it is anticipated that the desktop will become a device for biometric revenue derived from product sales and transactional authentication. Most middleware solutions leverage a variety of fingerprint solutions for desktop authentication.

Fingerprint is a proven technology capable of high levels of accuracy. Strong fingerprint solutions are capable of processing thousands of users without allowing a false match, and can verify nearly 100% of users with one or two placements of a finger. Because of this, many fingerprint technologies can be deployed in application where either security or convenience is the primary driver. Reduced size and power requirements, along with fingerprintâ„¢s resistance to environmental changes such as background light and temperature, allow the technology to be deployed in a range of logical and physical access environments. Fingerprint acquisition devices have grown quite small sensors slightly thicker than a coin, and smaller than 1.5 cm x 1.5 cm, are capable of acquiring and processing images. Thus fingerprint has emerged as a highly distinctive identifier, and classification, analysis and study of fingerprints has existed for decades.


9. REFERENCES
1. Electronics for you “ June 2002
2. RSA Securityâ„¢s official guide to CRYPTOGRAPHY BY Steve Burnett and Stephen Paine.
3. Infokairali “ December 2001.
4. http:// biometricgroup.com.
5. Encarta Encyclopedia 2002.
6. http:// howstuffworks.com.
7. http: // BiometricID.org.










ACKNOWLEDGEMENT

I express my sincere gratitude to Dr.Nambissan, Prof. & Head, Department of Electrical and Electronics Engineering, MES College of Engineering, Kuttippuram, for his cooperation and encouragement.
I would also like to thank my seminar guide Mrs. Renuka.T.K. (Lecturer, Department of EEE), Asst. Prof. Gylson Thomas. (Staff in-charge, Department of EEE) for their invaluable advice and wholehearted cooperation without which this seminar would not have seen the light of day.
Gracious gratitude to all the faculty of the department of EEE & friends for their valuable advice and encouragement.





CONTENTS

1. INTRODUCTION 1
2. IDENTIFICATION AND VERIFICATION SYSTEMS 2
3. BIOMETRIC SYSTEMS AND DEVICES 3
4. BIOMETRIC ACCURACY 4
5. FINGERPRINT VERIFICATION 5
6. FINGERSCAN 6
6.1 THE TECHNOLOGY BEHIND FINGERSCAN 6
6.2 THE ALGORITHMS 7
6.3 SYSTEM FUNCTIONS 8
6.4 MANAGEMENT CONTROL 10
6.5 SECURITY 11
6.6 AN OVERVIEW OF FINGERSCAN TECHNOLOGIES 13
6.7 CAPACITANCE SCANNER 13
6.8 ADVANTAGES OF FINGERPRINT SCANNERS 16
6.9 DISADVANTAGES 16

7. FUTURE APPLICATIONS 17
8. CONCLUSION 19
9. REFERENCES 20

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Biometric Fingerprint Identification

ABSTRACT



Identification of individuals is a very basic societal requirement. Electronic verification of a personâ„¢s identity is of great importance as more interactions take place electronically. Biometric fingerprint identification is a technique used to change the physical attribute of a person ie; his finger print into electronic processes. Until recently electronic verification was based on something the person had in their possession like a password. But the problem is that these forms arenâ„¢t very secure because passwords can be forgotten or taken away. In biometric finger print identification a scanner is used to get the image of the finger. When a user places his or her finger on the terminals of scanner the image is electronically read, analysed and compared with a previously recorded image of the same finger, which has been stored in finger scan data base. Finger print is a proven technology capable of high levels of accuracy.


1. INTRODUCTION
Positive identification of individuals is a very basic societal requirement. 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 can be catastrophic, and may include loss of confidential information, denial of service, and compromised data integrity. The value of reliable user authentication is not limited to just computer or network access. Many other applications in every day life also require user authentication, such as banking, e-commerce, and could benefit from enhanced security.

In fact, as more interactions take electronically, it becomes even more important to have an electronic verification of a person’s identity. Until recently, electronic verification took one of two forms. It was based on something the person had in their possession, like a magnetic swipe card, or something they knew, like a password. The problem is, these forms of electronic identification are not very secure, because they can be given away, taken away, or lost and motivated people have found ways to forge or circumvent these credentials.

The ultimate form of electronic verification of a person’s is biometrics. Biometrics refers to the automatic identification of a person based on his/her physiological or behavioral characteristics such as finger scan, retina, iris, voice scan, signature scan etc. By using this technique physiological characteristics of a person can be changed into electronic processes that are inexpensive and easy to use. People have always used the brain’s innate ability to recognize a familiar face and it has long been known that a person’s fingerprints can be used for identification




Reference: http://studentbank.in/report-biometric-f...z11a0riFOJ

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Fingerprint identification using biometrics
Abstract
What is biometrics identification?
 It is the process by which a person can be identified by his characteristics.
 The characteristic is scanned so that the computer can compare it to the data already stored in the database.
 Biometrics identification is more secure method of identifying a person , it can not be easily shared , traded or stolen by another.
Categories
 There are mainly two categories of biometrics identification –
 Physiological characteristics :- identified by physical traits of person.
E.g. Fingerprint , retinal scans , hand print scans.
 Behavioral characteristics :- identified with the things that can change with the environment.
E.g. Voice recognition , verifying signature.
history
 Possibly the most primary known instance of biometrics in practice was a form of finger printing being used in China in 14th century as reported by explorer Joao de Barros.
 Up until the late 1800’s identification largely relied upon “Photographic memory”.
 Bertillon developed a technique of multiple body measurements which later got named after him Bertillon-age.
Biometrics fingerprint identification
The science of fingerprint identification stands out among all other forensic sciences for many reasons as –
 Has served all governments worldwide to provide accurate identification of criminals.
 Establish the first forensic professional organization , IAI(International Association for Identification) in 1915.
 Worldwide , fingerprints harvested from crime “scenes lead to more suspect & generate more evidence in court than all other forensic techniques combined ”.
 It quickly & correctly identify two different people who look exactly alike.
Fingerprint matching
 Everyone is known to have unique , immutable fingerprints.
 A fingerprint is made up of a series of ridges and furrows on the surface of the finger.
 The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as the minutiae points .
 Minutiae points are local ridge characteristics that occur at either a ridge bifurcation or ridge ending.
 Fingerprint matching has two categories:- minutiae-based and correlation-based.
Fingerprint classification
 Fingerprint classification is a technique to assign a fingerprint into one of the several pre-specified types already established in the literature which can provide an indexing mechanism.
 To reduce the search time & computational complexity , it is desirable to classify these fingerprints in an accurate & consistent manner so that the input fingerprint is required to be matched only with a subset of the fingerprints in the database.
 To classify fingerprints an algorithm is used by which the fingerprints are classified into five classes , namely – whorl , right-loop , left-loop , arch ,tented-arch.
 The algorithm separates the no. of ridges present in 4 directions(0 deg , 45 deg , 90 deg, 135 deg)by filtering the central part of fingerprint with the bank of Gabor filters.
 This information is quantized to generate a Finger-Code which is used for classification.
Fingerprint enhancements
 A critical step in automatic fingerprint matching is to automatically & reliably extract minutiae from the input fingerprint images.
 The performance of a minutiae extraction algorithm relies heavily on the quality of the input fingerprint images.
 We have developed a fast fingerprint enhancement algorithm , which can adaptively improve clarity of ridge & furrow structure of input fingerprint images based on the estimated local ridge orientation & frequency.
Fingerprint types
 Latent Prints
 Patent Prints
 Plastic Prints
 Fingerprint capture & detection
Fingerprint detection
 The general structure of fingerprint scanner
 Advantages & disadvantages
 Acceptance
 Accuracy
 Ease of use
 Installation
 Training
 Uniqueness
 Security
 Acceptance
 Injury
 security
Fingerprint application
Biometrics iris recognition
 Iris recognition today combines technologies from several fields including , computer vision(CV) , pattern recognition , statistical interference , & optics . The goal of the technology is near-instant , highly accurate recognition of a person’s identity based on a digitally represented images of scanned eye.
 The tech. is based on the fact that no two iris patterns are alike.
 The iris is protected organ which makes identification possibilities life long.
 The iris can there for serve as a life long password which the person must never remember.
 Iris recognition system use small , high-quality cameras to capture a black & white high-resolution photograph of the iris.
 This technology is considered to be one of the safest , fastest , & most accurate , non invasive biometric technologies.
 They are used in passport , aviation security , access security , hospitals & national watch list.
 Iris recognition algorithm can be seen in more & more identification system relating to customs and immigration.
Iris recognition instruments
Advantages & disadvantages
 Highly protected
 Externally visible
 Variability
 Entropy
 Pre-natal morphogenesis
 Decidability index
 Image analysis & encoding time
 Changing pupil size confirms natural physiology
 Small target
 Moving target .. within another ..on yet another
 Located behind curved , wet , reflecting surface
 Obscured by eyelashes , lenses , reflections
 Partially occluded by eye leads often drooping
 Deforms non-elastically as pupil changes size
 Some -ve connotations
 Illumination should not be visible or bright
Future trends
 E-commerce
 Information security(info-sec)
 Authorization
 Building entry
 Automobile ignition
 Forensic application
 Computer network access
 Personal passwords

Presented By
Vinay Goel

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[attachment=13414]
Biometric Recognition – Fingerprints
Introduction
Finger print recognization system is under biometric application used to increase the user security.
Generally the biometric systems operating in two modes,
Verification
Identification.
Verification: The person to claim identity through an Number(identification number), user name etc…the system then gathers the input data and compares it in priviously stored data then give the result related data. if the data not related to template data it it simply denied.
Identification:
If the input data matches any of the template data sets, the system will allow access
OBJECTIVE
The system processes the data and collects the identifying features of the fingerprint. Next, it compares this information to previously stored information from various fingerprints. After making the comparison, the system determines if the input image matches the data of a fingerprint already in the database.
A few different processing methods are used to extract the identifying features, and the performance of each technique is analyzed.
FINGERPRINT
A fingerprint pattern is comprised of a sequence of Ridges and Valleys.
In a fingerprint image, the ridges appear as dark lines while the valleys are the light areas between the ridges.
The fingerprint image will have one or more regions where the ridge lines have a distinctive shape. These shapes are usually characterized by areas of high curvature or frequent ridge endings and are known as singular regions.
The project will be proceeded by using Matching techniques
There are two types of Finger print Matching techniques
Minutiae Base
Image Based
MINUTIAE
In this project we are implementing the Minutiae matching technique
It is first necessary to apply several pre-processing steps to the original fingerprint image to produce consistent results
Such steps generally include
Binarization
Noise removal
Thinning
Binarization
Image binarization is the process of turning a grayscale image to a black and white image.
In a gray-scale image, a pixel can take on 256 different intensity values while each pixel is assigned to be either black or white in a black and white image.
This conversion from gray-scale to black and white is performed by applying a threshold value to the image.
A critical component in the binarization process is choosing a correct value for the threshold.
The threshold values used in this study were selected empirically by trial and error.
After binarization, another major pre-processing technique applied to the image is thinning, which reduces the thickness of all ridge lines
This thinning method to be done with
Block Filtering method attempts to preserve the outermost pixels along each ridge
This is done with the following steps.
Step One: ridge width reduction
This step involves applying a morphological process to the image to reduce the width of the ridges
Morphological is a means of changing a stem to adjust its meaning to fit its syntactic and communicational context
Two basic morphological processes are
Erosion
Dilation
Dilation A dilation process is used to thicken the area of the valleys in the fingerprint.
Erosion:
Erosion thins objects in a binary image (ridge)
In this project we are using the Dilation
Original gray level image Image found after applying valley dilation
Step Two: passage of block filter
The next step involves performing a pixel-by pixel scan for black pixels across the entire image
Note that in MATLAB, image rows are numbered in increasing order beginning with the very top of the image as row one.
Similarly, columns are numbered in increasing order beginning with the leftmost side of the image as column one
The left to right scan continues until it covers the entire image. Next, a similar scan is performed across the image from right to left beginning at the pixel in row one and the last column.
Step Three: removal of isolated noise
Step Four: scan combination
A value of two means that the pixel from each scan was white, while a value of zero indicates the pixel from each scan was black. Meanwhile, a value of one means that the pixel from one scan was black while the same pixel from the other scan was white.
As a result, the new matrix needs to be adjusted to represent a valid binary image containing only zeros and ones. Specifically, all zeros and ones are assigned a value of zero (black pixel), and all twos are assigned a value of one (white pixel).
Step Five: elimination of one pixel from two-by-two squares of black
Next, a new scan is conducted on the combined image to detect two-by-two blocks of black pixels which represent a location where a ridge has not been thinned to a one-pixel width. It is likely that some of these two-by two blocks were created by the combination of the previous scans. This problem can be compensated for by changing one pixel within the block from black to white, which reduces the width at that particular point from two pixels to one. At the same time, this process needs to be implemented in a manner that preserves the overall ridge structure.
This operation can be performed by analyzing the pixels touching each individual black pixel. Note that each black pixel touches the three other black pixels within the two-by-two block. Therefore, there are only five other pixels thatcontain useful information.
Step Six: removal of unwanted spurs
Final Noise Removal
MINUTIAE EXTRACTION
The minutiae information can be extracted and stored after the image pre-processing is complete. This information consists of the following for each minutia:
• Location within the image
• Orientation angle
• Type (termination or bifurcation)

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Fingerprint: Finger Biometrics
Fingerprint Identification
Among all the biometric techniques, fingerprint-based identification is the oldest method which has been successfully used in numerous applications.
Everyone is known to have unique, immutable fingerprints.
A fingerprint is made of a series of ridges and furrows on the surface of the finger.
The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as the minutiae points.
Minutiae points are local ridge characteristics that occur at either a ridge bifurcation or a ridge ending.
Fingerprint Basics
A fingerprint has many identification and classification basics
Fingerprint Basics (minutiae)
Fingerprint Basics (minutiae)
Fingerprint Basics (minutiae)
Fingerprint Basics
How many different ridge characteristics can you see?
Fingerprint Identifications
A single rolled fingerprint may have as many as 100 or more identification points that can be used for identification purposes.
There is no exact size requirement as the number of points found on a fingerprint impression depend on the location of the print.
As an example the area immediately surrounding a delta will probably contain more points per square millimetre than the area near the tip of the finger which tends to not have that many points. 
Fingerprint Representation
Fingerprinting was first created by Dr. Henry Fault, a British surgeon.
The general shape of the fingerprint is generally used to pre-process the images, and reduce the search in large databases.
These are:
Loop
Whorl
arch

There are several sub-categories of the above including:
right loop,
left loop,
Single or double whorl
Plain or tented arch
Ulnar or radial loops
The loop is by far the most common type of fingerprints.
The human population has fingerprints in the following percentages:
Loop – 65%
Whorl -- 30%
Arch -- 5%
Class Activity (15 minutes)
Classify the following fingerprints
Classify your right hand fingerprints
Check and classify your partner's right hand fingers.
Hand in your classification of your right hand finger – after being checked by your partner.
Fingerprint matching techniques
There are two categories of fingerprint matching techniques: minutae-based and correlation based.
Minutiae-based techniques first find minutiae points and then map their relative placement on the finger. 
The correlation-based method is able to overcome some of the difficulties of the minutiae-based approach. 
Fingerprint Processing
Minutiae-based processing has problems including:
In real life you would have impressions made at separate times and subject to different pressure distortions.
On the average, many of these images are relatively clean and clear, however, in many of the actually crime scenes, prints are anything but clear.
There are cases where it is not easy to have a core pattern and a delta but only a latent that could be a fingertip, palm or even foot impression
The method does not take into account the global pattern of ridges and furrows.
Fingerprint matching based on minutiae has problems in matching different sized (unregistered) minutiae patterns.
Local ridge structures can not be completely characterized by minutiae.
The solution is to find an alternate representation of fingerprints which captures more local information and yields a fixed length code for the fingerprint.
Fingerprint Processing
Correlation-based processing has its own problems including:
Correlation-based techniques require the precise location of a registration point
It is also affected by image translation and rotation.
Fingerprint Processing
Human fingerprints are unique to each person and can be regarded as some sort of signature, certifying the person's identity.
Because straightforward matching between the fingerprint pattern to be identified and many already known patterns has problems due to its high sensitivity to errors (e.g. various noises, damaged fingerprint areas, or the finger being placed in different areas of fingerprint scanner window and with different orientation angles, finger deformation during the scanning procedure etc.).
Modern techniques focus on extracting minutiae points (points where capillary lines have branches or ends) from the fingerprint image, and check matching between the sets of fingerprint features.
A good reliable fingerprint processing technique requires sophisticated algorithms for reliable processing of the fingerprint image:
noise elimination,
minutiae extraction,
rotation and translation-tolerant fingerprint matching.
At the same time, the algorithms must be as fast as possible for comfortable use in applications with large number of users. It must also be able to fit into a microchip.
Progressive Fingerprint Matching
Image Processing
Capture the fingerprint images and process them through a series of image processing algorithms to obtain a clear unambiguous skeletal image of the original gray tone impression, clarifying smudged areas, removing extraneous artifacts and healing most scars, cuts and breaks.
Minutiae Extraction
Feature Detection for MatchingRidge ends and bifurcations (minutiae) within the skeletal image are identified and encoded, providing critical placement, orientation and linkage information for the fingerprint matching process.
Matching Fingerprint Search
The fingerprint matcher compares data from the input search print against all appropriate records in the database to determine if a probable match exists.
Minutia relationships, one to another are compared. Not as locations within an X-Y co-ordinate framework, but as linked relationships within a global context.
Each template comprises a multiplicity of information chunks, every information chunk representing a minutia and comprising a site, a minutia slant and a neighborhood.
Each site is represented by two coordinates. [ l = (x,y)]
The neighborhood comprises of positional parameters with respect to a chosen minutia for a predetermined figure of neighbor minutiae. In single embodiment, a neighborhood border is drown about the chosen minutia and neighbor minutiae are chosen from the enclosed region. [ theta]
A live template is compared to a stored measured template chunk-by-chunk. A chunk from the template is loaded in a random access memory (RAM).
The site, minutia slant and neighborhood of the reference information chunk are compared with the site, minutia slant and neighborhood of the stored template ( latent) information chunk by information chunk.
The neighborhoods are compared by comparing every positional argument. If every the positional parameters match, the neighbors match. If a predetermined figure of neighbor matches is met, the neighborhoods match.
If the matching rate of all information chunks is equivalent to or superior to the predetermined information chunk rate, the live template matches the stored (latent) template.
A selected fingerprint is mapped into a digital frame by a function f (minutiea type t, site l, neighborhood theta) =
f( t, l, theta).
Fingerprint Classification:
Large volumes of fingerprints are collected and stored everyday in a wide range of applications including forensics, access control, and driver license registration.
An automatic recognition of people based on fingerprints requires that the input fingerprint be matched with a large number of fingerprints in a database (FBI database contains approximately 70 million fingerprints!).
To reduce the search time and computational complexity, it is desirable to classify these fingerprints in an accurate and consistent manner so that the input fingerprint is required to be matched only with a subset of the fingerprints in the database.
Fingerprint Characteristics
Biometric (Fingerprint) Strengths
Finger tip most mature measure
Accepted reliability
High quality images
Small physical size
Low cost
Low False Acceptance Rate (FAR)
Small template (less than 500 bytes)
Biometric (Fingerprint weaknesses)
Requires careful enrollment
Potential high False Reject Rate (FRR) due to:
Pressing too hard, scarring, misalignment, dirt
Vendor incompatibility
Cultural issues
Physical contact requirement a negative in Japan
Perceived privacy issues with North America
Fingerprint Technology
As fingerprint technology matures, veriations in the technology also increase including:
Optical – finger is scanned on a platen ( glass, plastic or coasted glass/plastic).
Silicon – uses a silicon chip to read the capacitance value of the fingerprint. There are two types of this:
Active capacitance
Passive capacitance
Ultrasound – requires a large scanning device. It is appealing because it can better permeate dirt.
Class Activity
In groups of twos – discuss and write down the many uses of fingerprint technology.

I. Introduction
Fingerprints are imprints formed by friction ridges of the skin and thumbs. They
have long been used for identification because of their immutability and individuality.
Immutability refers to the permanent and unchanging character of the pattern on each
finger. Individuality refers to the uniqueness of ridge details across individuals; the
probability that two fingerprints are alike is about 1 in 1.9x1015.
However, manual fingerprint verification is so tedious, time consuming and expensive that
is incapable of meeting today’s increasing performance requirements. An automatic
fingerprint identification system is widely adopted in many applications such as building or
area security and ATM machines [1-2].
Two approaches will be described in this project for fingerprint recognition:
• Approach 1: Based on minutiae located in a fingerprint
• Approach 2: Based on frequency content and ridge orientation of a fingerprint
II. First Approach
Most automatic systems for fingerprint comparison are based on minutiae matching
Minutiae are local discontinuities in the fingerprint pattern. A total of 150 different
minutiae types have been identified. In practice only ridge ending and ridge bifurcation
minutiae types are used in fingerprint recognition. Examples of minutiae are shown in
figure 1.



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