i need the seminar topic of AN ATM WITH AN EYE..please give me the entire report of this

[attachment=3789]
AN ATM WITH AN EYE
INTRODUCTION
ATM is a machine which made money transactions easy for customers of bank.But security is an issue that has to be addressed. In the traditional way, security is handled by requiring the combination of a physical access
card and a PIN or other password. The fallacies of this system is stolen cards, badly-chosen or automatically assigned PINs, cards with little or no encryption schemes, employees with access to non-encrypted customer account information etc which can cause problems. In this paper, a physical access card, a PIN, and electronic facial recognition are used for authentication. By forcing
the ATM to match a live image of a customerâ„¢s face with an image stored in a bank database corresponding to the customer. The main challenges to this technique are:
-allowing for an appropriate level
of variation in a customerâ„¢s face when compared to the database image
-keeping verification process very fast.
-opportunity to acquire a photo for each and every customer

METHODOLOGY USED
locate a powerful
open-source facial recognition program that uses local feature analysis and that is
targeted at facial verification which are compilable on multiple
systems, including Linux and Windows variants. Several
sample images will be taken of several individuals to be used as test cases for testing and evaluating the software. The ATM black box
program will serve as the theoretical ATM with which the facial
recognition software will interact. Both pieces of software will be compiled and run on a Windows XP and a Linux
system.the black boxes will be broken into two components “ a server
and a client “ to be used in a two-machine network. DES encryption to the client end to
encrypt the input data and decrypt the output data from the server will be done.

AN ATM WITH AN EYE




ABSTRACT

There is an urgent need for improving security in banking region. With the advent of ATM though banking became a lot easier it even became a lot vulnerable. The chances of misuse of this much hyped 'insecure' baby product (ATM) are manifold due to the exponential growth of 'intelligent' criminals day by day. ATM systems today use no more than an access card and PIN for identity verification. This situation is unfortunate since tremendous progress has been made in biometric identification techniques, including finger printing, retina scanning, and facial recognition. This paper proposes the development of a system that integrates facial recognition technology into the identity verification process used in ATMs. The development of such a system would serve to protect consumers and financial institutions alike from fraud and other breaches of security.














1. INTRODUCTION

The rise of technology in India has brought into force many types of equipment that aim at more customer satisfaction. ATM is one such machine which made money transactions easy for customers to bank. The other side of this improvement is the enhancement of the culprit's probability to get his 'unauthentic' share. Traditionally, security is handled by requiring the combination of a physical access card and a PIN or other password in order to access a customer's account. This model invites fraudulent attempts through stolen cards, badly-chosen or automatically assigned PINs, cards with little or no encryption schemes, employees with access to non-encrypted customer account information and other points of failure.
Our paper proposes an automatic teller machine security model that would combine a physical access card, a PIN, and electronic facial recognition. By forcing the ATM to match a live image of a customer's face with an image stored in a bank database that is associated with the account number, the damage to be caused by stolen cards and PINs is effectively neutralized. Only when the PIN matches the account and the live image and stored image match would a user be considered fully verified.
The main issues faced in developing such a model are keeping the time elapsed in the verification process to a negligible amount, allowing for an appropriate level of variation in a customer's face when compared to the database image, and that credit cards which can be used at ATMs to withdraw funds are generally issued by institutions that do not have in-person contact with the customer, and hence no opportunity to acquire a photo.
Because the system would only attempt to match two (and later, a few) discrete images, searching through a large database of possible matching candidates would be unnecessary. The process would effectively become an exercise in pattern matching, which would not require a great deal of time. With appropriate lighting and robust learning software, slight variations could be accounted for in most cases. Further, a positive visual match would cause the live image to be stored in the database so that future transactions would have a broader base from which to compare if the original account image fails to provide a match - thereby decreasing false negatives.
When a match is made with the PIN but not the images, the bank could limit transactions in a manner agreed upon by the customer when the account was opened, and could store the image of the user for later examination by bank officials. In regards to bank employees gaining access to customer PINs for use in fraudulent transactions, this system would likewise reduce that threat to exposure to the low limit imposed by the bank and agreed to by the customer on visually unverifiable transactions.
In the case of credit card use at ATMs, such a verification system would not currently be feasible without creating an overhaul for the entire credit card issuing industry, but it is possible that positive results (read: significant fraud reduction) achieved by this system might motivate such an overhaul.
The last consideration is that consumers may be wary of the privacy concerns raised by maintaining images of customers in a bank database, encrypted or otherwise, due to possible hacking attempts or employee misuse. However, one could argue that having the image compromised by a third party would have far less dire consequences than the account information itself. Furthermore, since nearly all ATMs videotape customers engaging in transactions, it is no broad leap to realize that banks already build an archive of their customer images, even if they are not necessarily grouped with account information.


2. LITERATURE REVIEW

For most of the past ten years, the majority of ATMs used worldwide ran under IBM's now-defunct OS/2. However, IBM hasn't issued a major update to the operating system in over six years. Movement in the banking world is now going in two directions: Windows and Linux. NCR, a leading world-wide ATM manufacturer, recently announced an agreement to use Windows XP Embedded in its next generation of personalized ATMs (crmdaily.com.) Windows XP Embedded allows OEMs to pick and choose from the thousands of components that make up Windows XP Professional, including integrated multimedia, networking and database management functionality. This makes the use of off-the-shelf facial recognition code more desirable because it could easily be compiled for the Windows XP environment and the networking and database tools will already be in place.
For less powerful ATMs, KAL, a software development company based in Scotland, provides Kalignite CE, which is a modification of the Windows CE platform. This allows developers that target older machines to more easily develop complex user-interaction systems . Many financial institutions are relying on a third choice, Windows NT, because of its stability and maturity as a platform.
On an alternative front, the largest bank in the south of Brazil, Banrisul, has installed a custom version of Linux in its set of two thousand ATMs, replacing legacy MS-DOS systems. The ATMs send database requests to bank servers which do the bulk of transaction processing (linux.org.) This model would also work well for the proposed system if the ATMs processors were not powerful enough to quickly perform the facial recognition algorithms.
In terms of the improvement of security standards, MasterCard is spearheading an effort to heighten the encryption used at ATMs. For the past few decades, many machines have used the Data Encryption Standard developed by IBM in the mid 1970s that uses a 56-bit key. DES has been shown to be rather easily cracked, however, given proper computing hardware. In recent years, a "Triple DES" scheme has been put forth that uses three such keys, for an effective 168-bit key length. MasterCard now requires new or relocated ATMs to use the Triple DES scheme, and by April, 2005, both Visa and MasterCard will require that any ATM that supports their cards must use Triple DES. ATM manufacturers are now developing newer models that support Triple DES natively; such redesigns may make them more amenable to also including snapshot cameras and facial recognition software, more so than they would be in regards to retrofitting pre-existing machines .
There are hundreds of proposed and actual implementations of facial recognition technology from all manner of vendors for all manner of uses. However, for the model proposed in this paper, we are interested only in the process of facial verification - matching a live image to a predefined image to verify a claim of identity - not in the process of facial evaluation - matching a live image to any image in a database. Further, the environmental conditions under which the verification takes place - the lighting, the imaging system, the image profile, and the processing environment - would all be controlled within certain narrow limits, making hugely robust software unnecessary .One leading facial recognition algorithm class is called image template based. This method attempts to capture global features of facial images into facial templates. Neural networks, among other methods, are often used to construct these templates for later matching use. An alternative method, called geometry-based, is to explicitly examine the individual features of a face and the geometrical relationship between those features (Gross.) What must be taken into account, though, are certain key factors that may change across live images: illumination, expression, and pose (profile.)
A study was recently conducted of leading recognition algorithms, notably one developed by two researchers at MIT, Baback Moghaddam and Alex Pentland, and one a commercial product from Identix called Facelt. The MIT program is based on Principal Feature Analysis, an adaptation of template based recognition. FaceIt's approach uses geometry-based local feature analysis. Both algorithms have to be initialized by providing the locations of the eyes in the database image, from which they can create an internal representation of the normalized face. It is this representation to which future live images will be compared .
In the study, it was found that both programs handled changes in illumination well. This is important because ATM use occurs day and night, with or without artificial illumination. Likewise, the programs allowed general expression changes while maintaining matching success. However, extreme expressions, such as a scream profile, or squinted eyes, dropped the recognition rates significantly. Lastly, matching profile changes worked reasonably well when the initial training image(s) were frontal, which allowed 70-80% success rates for up to 45 degrees of profile change... however, 70-80% success isn't amenable to keeping ATM users content with the system.
The natural conclusion to draw, then, is to take a frontal image for the bank database, and to provide a prompt to the user, verbal or otherwise, to face the camera directly when the ATM verification process is to begin, so as to avoid the need to account for profile changes. With this and other accommodations, recognition rates for verification can rise above 90%. Also worth noting is that FaceIt's local feature analysis method handled variations in the test cases slightly better than the PGA system used by the MIT researchers .
Another paper shows more advantages in using local feature analysis systems. For internal representations of faces, LFA stores them topographically; that is, it maintains feature relationships explicitly. Template based systems, such as PGA, do not. The advantages of LFA are that analysis can be done on varying levels of object grouping, and that analysis methods can be independent of the topography. In other words, a system can examine just the eyes, or the eyes nose and mouth, or ears, nose, mouth and eyebrows, and so on, and that as better analysis algorithms are developed, they can fit within the data framework provided by LFA
The conclusion to be drawn for this project, then, is that facial verification software is currently up to the task of providing high match rates for use in ATM transactions. What remains is to find an appropriate open-source local feature analysis facial verification program that can be used on a variety of platforms, including embedded processors, and to determine behavior protocols for the match / non-match cases.


3. OUR METHODOLOGY
The first and most important step of this project will be to locate a powerful open-source facial recognition program that uses local feature analysis and that is targeted at facial verification. This program should be compilable on multiple systems, including Linux and Windows variants, and should be customizable to the extent of allowing for variations in processing power of the machines onto which it would be deployed.
We will then need to familiarize ourselves with the internal workings of the program so that we can learn its strengths and limitations. Simple testing of this program will also need to occur so that we could evaluate its effectiveness. Several sample images will be taken of several individuals to be used as test cases - one each for "account" images, and several each for "live" images, each of which would vary pose, lighting conditions, and expressions.
Once a final program is chosen, we will develop a simple ATM black box program. This program will server as the theoretical ATM with which the facial recognition software will interact. It will take in a name and password, and then look in a folder for an image that is associated with that name. It will then take in an image from a separate folder of "live" images and use the facial recognition program to generate a match level between the two. Finally it will use the match level to decide whether or not to allow "access", at which point it will terminate. All of this will be necessary, of course, because we will not have access to an actual ATM or its software.
Both pieces of software will be compiled and run on a Windows XP and a Linux system. Once they are both functioning properly, they will be tweaked as much as possible to increase performance (decreasing the time spent matching) and to decrease memory footprint.
Following that, the black boxes will be broken into two components - a server and a client - to be used in a two-machine network. The client code will act as a user interface, passing all input data to the server code, which will handle the calls to the facial recognition software, further reducing the memory footprint and processor load required on the client end. In this sense, the thin client architecture of many ATMs will be emulated.
We will then investigate the process of using the black box program to control a USB camera attached to the computer to avoid the use of the folder of "live" images. Lastly, it may be possible to add some sort of DES encryption to the client end to encrypt the input data and decrypt the output data from the server - knowing that this will increase the processor load, but better allowing us to gauge the time it takes to process.

4. CONCLUSION
We thus develop an ATM model that is more reliable in providing security by using facial recognition software. By keeping the time elapsed in the verification process to a negligible amount we even try to maintain the efficiency of this ATM system to a greater degree.

[/font]Hello sir................i want more details abt an atm with an eye plz sir send me a full report.............

[attachment=10003]
An ATM with an eye
The rise of technology has brought into force many types of equipment that aim at more customer satisfaction. ATM is one such machine which made money transactions easy for customers to bank. The other side of this improvement is the enhancement of the culprit's probability to get his 'unauthentic' share. Traditionally, security is handled by requiring the combination of a physical access card and a PIN or other password in order to access a customer's account. This model invites fraudulent attempts through stolen cards, badly-chosen or automatically assigned PINs, cards with little or no encryption schemes, employees with access to non-encrypted customer account information and other points of failure.
Our paper proposes an automatic teller machine security model that would combine a physical access card, a PIN, and electronic facial recognition. By forcing the ATM to match a live image of a customer's face with an image stored in a bank database that is associated with the account number, the damage to be caused by stolen cards and PINs is effectively neutralized. Only when the PIN matches the account and the live image and stored image match would a user be considered fully verified.
The main issues faced in developing such a model are keeping the time elapsed in the verification process to a negligible amount, allowing for an appropriate level of variation in a customer's face when compared to the database image, and that credit cards which can be used at ATMs to withdraw funds are generally issued by institutions that do not have in-person contact with the customer, and hence no opportunity to acquire a photo.
Because the system would only attempt to match two (and later, a few) discrete images, searching through a large database of possible matching candidates would be unnecessary. The process would effectively become an exercise in pattern matching, which would not require a great deal of time. With appropriate lighting and robust learning software, slight variations could be accounted for in most cases. Further, a positive visual match would cause the live image to be stored in the database so that future transactions would have a broader base from which to compare if the original account image fails to provide a match - thereby decreasing false negatives.
When a match is made with the PIN but not the images, the bank could limit transactions in a manner agreed upon by the customer when the account was opened, and could store the image of the user for later examination by bank officials. In regards to bank employees gaining access to customer PINs for use in fraudulent transactions, this system would likewise reduce that threat to exposure to the low limit imposed by the bank and agreed to by the customer on visually unverifiable transactions.
In the case of credit card use at ATMs, such a verification system would not currently be feasible without creating an overhaul for the entire credit card issuing industry, but it is possible that positive results (read: significant fraud reduction) achieved by this system might motivate such an overhaul.
The last consideration is that consumers may be wary of the privacy concerns raised by maintaining images of customers in a bank database, encrypted or otherwise, due to possible hacking attempts or employee misuse. However, one could argue that having the image compromised by a third party would have far less dire consequences than the account information itself. Furthermore, since nearly all ATMs videotape customers engaging in transactions, it is no broad leap to realize that banks already build an archive of their customer images, even if they are not necessarily grouped with account information.

[attachment=10013]
ATM Facelifts
Improve your brand with a facelift
Available facelift options include:
• Exchanging of existing fascia
• The replacement fascia is painted with industrial paint in a controlled environment room at the ACG warehouse
• Re-finish the exterior of the ATM with 2 part industrial paint
• New camera window
• Replacement of light diffuser
• New Braille
• Re-install new stickers as provided
• Delivery of new parts and installation
• Upgrading the monitor to color LCD
Iris Recognition ATM's Help Keep an Eye on Crime:
RALEIGH — The secret code you use to get money from an ATM is about to get more secure -- with a code you'll never forget. Within a couple of months, some major banks will introduce "pin-less" ATM's. WRAL's Tom Lawrence has details of a breakthrough in ATM security that lets you get your cash with the blink of your eye.
Americans made 11 billion ATM transactions last year. And ATM fraud is on the rise. A new technology called Iris Recognition records your eye for identification. Sensar's Tom Drury explains.
Listen toauorRealAudiofiles."An iris pattern is highly distinctive. Ten times more unique than a fingerprint. Because it is so distinctive, we can tell with absolute accuracy that you are who you say you are."With PIN numbers we now use, thieves can steal your number.
The key to your ATM account is the personal identification number or pin. A crook with a high powered video camera, hidden from view, can record you putting the number in, unless you cover your hand. With iris recognition, a video camera safely records an image of your iris. That's the colored part of your eye.
Drury says a computer uses a complex formula to turn light and dark flecks of the iris into digital code.
Listen toauorRealAudiofiles."We create, effectively, a human bar code and that human bar code becomes your identity inside the ban."In seconds your iris code is compared with the bank's copy.
Listen toauorRealAudiofiles."As I look at the yellow light, it's taking my picture. It's finished and the screen recognized Tom Drury. That's me."Scientists who developed iris recognition say it's not only fraud proof, it's foolproof because you can't forget it like a PIN number.
Citibank plans to test Iris ID ATM's this summer. There's no word whether North Carolina banks will use the machines. They are already in use in Great Britain.

The last consideration is that consumers may be wary of the privacy concerns raised by maintaining images of customers in a bank database, encrypted or otherwise, due to possible hacking attempts or employee misuse. However, one could argue that having the image compromised by a third party would have far less dire consequences than the account information itself. Furthermore, since nearly all ATMs videotape customers engaging in transactions, it is no broad leap to realize that banks already build an archive of their customer images, even if they are not necessarily grouped with account information.

Reference: http://studentbank.in/report-atm-with-an...z1GMKwEegW

Hello sir............i want full report of atm with an eye my email id is swati.yals[at]gmail.com plz sir mail me...............

Presented By:
J MAHESH BABU

---

[attachment=11261]
 What is technology
EFFICIENTLY TECHNOLOGY IS THETECHNICAL MEANS PEOPLE USE TO IMPROVE THEIR SURROUNDING. IT IS ALSO THE KNOWLEDGE OF USING THE TOOLS AND MACHINES TO DO TASKS.
 Automated teller machine
 An automated teller machine (ATM) is a computerized telecommunications device that provides the customers of a financial institution with access to financial transactions in a public space without the need for a human clerk or bank teller.
 On most modern ATMs, the customer is identified by inserting a plastic ATM card with a magnetic stripe or a plastic smartcard with a chip, that contains a unique card number and some security information, such as an expiration date or CVVC (CVV). Security is provided by the customer entering a personal identification number (PIN).
An ATM With An Eye
 There is an urgent need for improving security in banking region. With the advent of ATM though banking became a lot easier it even became a lot vulnerable.
 The chances of misuse of this much hyped 'insecure' baby product (ATM) are manifold due to the exponential growth of 'intelligent' criminals day by day.
WHERE IT USE
 The development of such a system would serve to protect consumers and financial institutions alike from fraud and other breaches of security.
HOW IT WORK
 A camera based in cash machine will detect the pattren in second. and compare it with one store in a central computer. Or encode on to the cash card.
 A sensar system examines the randomly formed features of the iris of the eye . as unique as a fingerprint.
The progress has been made in biometric identification techniques, including finger printing, retina scanning, and facial recognition
 This paper proposes the development of a system that integrates facial recognition technology into the identity verification process used in ATMs
FUNCTION
AN ATW WITH EYE:THE BEST TECHNOLOGY
 Protecting Your Privacy: Keeping an Eye on Your Private Information.
 E-mail, the Internet, automated teller machines (ATM), computer banking.
 long distance carriers, even credit cards make our lives more efficient.
 keeping our private information confidential.
 Electronic transactions can leave you vulnerable to fraud and other crimes.
A Word On Passwords
 Whether you are on the Internet or an online banking program, you are often required to use a password
 The worst passwords to use are the ones that come to mind first -- name, spouse's name, maiden name, pets, children's name, even street addresses, etc.
 The best passwords mix numbers with upper and lowercase letters. A password that is not found in the dictionary is even better
How you protect your password
 Protect Your Personal Identification Number (PIN)
Protect Your ATM Cards
 An ATM card should be treated as thought it were cash. Avoid providing card and
account information to anyone over the telephone.
 When making a cash withdrawal at an ATM, immediately remove the case as
soon as the machine releases it. Put the case in your pocket and wait until you are in
a secure location before counting it. Never use an ATM in an isolated area or where people are loitering.
 Be sure to take your receipt to record transactions and match them against monthly statements. Dishonest people can use your receipt to get your account number. Never leave the receipt at the site.

presented by:
Suresh kumar

---

[attachment=11308]
An automated teller machine (ATM) or automatic banking machine (ABM) is a computerised telecommunications device that provides the clients of a financial institution with access to financial transactions in a public space without the need for a cashier, human clerk or bank teller.
On most modern ATMs, the customer is identified by inserting a plastic ATM card with a magnetic stripe or a plastic smart card with a chip, that contains a unique card number and some security information such as an expiration date or CVVC (CVV). Authentication is provided by the customer entering a personal identification number (PIN).
Using an ATM, customers can access their bank accounts in order to make cash withdrawals (or credit card cash advances) and check their account balances as well as purchase cell phone prepaid credit. Thus, ATMs often provide the best possible exchange rate for foreign travelers and are heavily used for this purpose as well.
ATMs are known by various other names including Automated Transaction Machine, automated banking machine, cashpoint (in Britain),money machine, bank machine, cash machine, hole-in-the-wall, Bancomat (in various countries in Europe and Russia), Multibanco (after a registered trade mark, in Portugal), and Any Time Money (in India)
Card fraud
In an attempt to prevent criminals from shoulder surfing the customer's PINs, some banks draw privacy areas on the floor. For a low-tech form of fraud, the easiest is to simply steal a customer's card. A later variant of this approach is to trap the card inside of the ATM's card reader with a device often referred to as a Lebanese loop. When the customer gets frustrated by not getting the card back and walks away from the machine, the criminal is able to remove the card and withdraw cash from the customer's account.
Another simple form of fraud involves attempting to get the customer's bank to issue a new card and stealing it from their mail.
Iris Recognition Properties of the iris
 Has highly distinguishing texture
 Right eye differs from left eye
 Twins have different iris texture
 Not trivial to capture quality image
 + Works well with cooperative subjects
 + Used in many airports in the world
Hardware
An ATM is typically made up of the following devices:
• CPU
• Magnetic and/or Chip card reader
• PIN Pad
• Secure cryptoprocessor,
• Display
• Function key buttons or a Touchscreen
• Record Printer
• Vault
• Housing
Represent iris texture as a binary vector of 2048 bits
Find (nearly circular) iris and create 8 bands or zones
Cross correlate 1024 local areas with a Gabor wavelet
Use 2nd directional derivative and 1st directional derivative
Summary of feature extraction
 Obtain quality image of certain (left) eye
 Find boundary of pupil and outside of iris
 Normalize radii to range, say, 0.5 to 1.0
 Define the 8 bands by radii ranges
 Perform 2 dot products at each of 1024 locations defined around the bands by radius rho and angle phi
How is the matching done to templates of enrolled persons?
 Person scanned under controlled environment and iris pattern is stored with ID (say address, SS#, etc.)
 Might be several million such templates for frequent flyers (6B for all world)
 At airport or ATM, scan unknown person’s left eye; then compute Hamming distance to ALL templates.
Distributions of true matches versus non matches
Design of former SENSAR ATM iris scanner
Recognition is possible by comparing unknown scan to MILLIONS of stored templates
 Less than 32% unmatched bits means “MATCH”
 Only need to count unmatched bits – use exclusive OR with machine words
 Mask off bad patches due to eyelid or eyelash interference (have to detect that)

Presented by:
OWAIS NOOR TRUMBOO

---

[attachment=11538]
INTRODUCTION
 ATM made money transaction easy.
 Other side is enhancement of culprit’s action to get unauthentic access.
 Improved Technology is used.
 Facial Recognition is recent technology used at ATMs.
ATM with facial Recognition Mechanism
History of an ATM
 Many inventors contribute to the invention.
 Don Wetzel invented first successful and modern ATM.
 But Luther GEORGE started patenting earlier in 1939.
 A working prototype was ready in 1969 & was installed in New York.
Don Wetzel's ATM
 He got the idea while waiting in line at Dallas Bank.
 It was not in a lobby.
 It was actually in the wall of the Bank.
 Had a canopy over IT for protection.
 ATM was cash dispenser only.
 First ATM’s were off-line machines.
What is an ATM
 A Banking Terminal that accepts deposits & dispenses cash.
 It has two input devices viz ;
1. A card reader that reads magnetic strip.
2. A keypad.
 It has four output devices viz ;
1. A speaker.
2. A display screen.
3. A printer.
4. A safe & cash dispensing mechanism.
Atm machine
ATM
 Networking
ATMs are connected to interbank network enabling people to withdraw and deposit money from machines not belonging to the bank where they have their account.
 Hardware
ATMs contain secure cryptoprocessors, generally within an IBM PC compatible host computer in a secure enclosure.
 Software
ATMs are moving away from custom circuit boards and into full-fledged with commodity operating systems such as Windows 2000 and Linux .
 Reliability
ATMs are generally reliable, but if they do go wrong customers will be left without cash or giving out higher value notes.
ATM Frauds
These had two common forms.
1. In the low-tech form, the user's PIN is observed by someone watching as they use the machine.
2. By contrast, the most common high-tech modus operandi involves the installation of a magnetic card reader over the real ATM's card slot, and the use of a wireless surveillance camera to observe the user's PIN.
ATM’s in Operation
Need of Facial Recognition at ATM
 Traditionally, card & PIN is used to access account.
 ATM frauds are increasing.
 By forcing ATM to match live image with an image stored in a bank database, frauds can be minimized.
 Only when PIN & image matches, user is considered fully verified.
Where Facial Recognition Technology is used
 Used mainly in Law Enforcement Agencies & Security Surveillance.
 Also has several other uses :
1. Eliminating voter fraud.
2. Check-Cashing identity verification.
3. Computer security.
 Also used to pinpoint the face in the crowd & measure its features.
Computer Security
 Facial Recognition software can be used to lock your computer.
Used to pinpoint faces in crowd
 Facial Recognition mechanism is also used to pinpoint a face in the crowd & measure its features.
Working
 Facial Recognition analyzes Characteristics of face through digital camera.
 There are peaks & valleys on face called nodal points.
 There are around 80 nodal points on face.
 Some of them are :
1. Distance between eyes.
2. Width of nose.
3. Depth of eye sockets.
4. Cheekbones.
5. Jaw line.
6. Chin.
 These nodal points are measured to create a numerical code that represents face in database.
 This code is called face-print.
 These measurements are then used for comparison when user stands before camera.
 Does not require physical contact with image capturing device.
Identification Steps
 Take a photo of the individual & encode it.
 Match the encoding against database.
 Display the result (i.e. verified or not-verified).
Verification
Advantages
 Frauds are minimized.
 It does not require any physical contact with image capture device.
 Does not require any advance hardware.
 Convenient to use because additional security information (smart cards, pwd. etc) can be skipped.
 Usually helps in situation where certain biometric feature is not optimal. e.g. hard workers may have raw finger prints.
Disadvantages
 Face is not so unique, so its recognition reliability is slightly lower.
 To match with the database image, it requires lot of time.
Conclusion
 As ATM frauds are increasing, or the culprit’s probability to get the ‘unauthentic access’ is increasing, additional security like Facial Recognition mechanism is necessity.

ppt and full report of ATM WITH AN EYE you can find in following pages ... i am giving this because its spread in different pages
http://studentbank.in/report-atm-with-an...4#pid11994
http://studentbank.in/report-atm-with-an-eye?page=2
http://studentbank.in/report-atm-with-an-eye?page=3
http://studentbank.in/report-an-atm-with-an-eye--18296
http://studentbank.in/report-an-atm-with-an-eye--17977
http://studentbank.in/report-an-atm-with-an-eye

hope you enjoy it all ...

hiiiiiiiiii
i need more info regarding an atm with an eye.itz urgent.




by
divya

ppt and full report of ATM WITH AN EYE you can find in following pages ... i am giving this because its spread in different pages
http://studentbank.in/report-atm-with-an...4#pid11994
http://studentbank.in/report-atm-with-an-eye?page=2
http://studentbank.in/report-atm-with-an-eye?page=3
http://studentbank.in/report-an-atm-with-an-eye--18296
http://studentbank.in/report-an-atm-with-an-eye--17977
http://studentbank.in/report-an-atm-with-an-eye

hope you enjoy it all ...

forward the abstract and report of atm with an eye

to get information about the topic An ATM With An Eye full report ,ppt ,related topic refer the page link bellow

http://studentbank.in/report-atm-with-an-eye

http://studentbank.in/report-atm-with-an-eye?page=3

http://studentbank.in/report-atm-with-an-eye?page=2

http://studentbank.in/report-an-atm-with-an-eye--18296

hello sir i am laxmi gujale i want full report on atm with an eye ,my email id is gujalelaxmi[at]gmail.com

sir,
I need ful reports of an atm with an eye seminar topic pls send to mail (laxmivastrad1993[at]gmail.com).tthikng to register.........

To get full information or details of atm with an eye please have a look on the pages


http://studentbank.in/report-atm-with-an-eye

http://studentbank.in/report-atm-with-an-eye?page=3

http://studentbank.in/report-atm-with-an-eye?page=2

http://studentbank.in/report-an-atm-with-an-eye--18296


if you again feel trouble on atm with an eye please reply in that page and ask specific fields in atm with an eye

(18-06-2010, 04:22 PM)computer science topics Wrote: AN ATM WITH AN EYE




ABSTRACT

There is an urgent need for improving security in banking region. With the advent of ATM though banking became a lot easier it even became a lot vulnerable. The chances of misuse of this much hyped 'insecure' baby product (ATM) are manifold due to the exponential growth of 'intelligent' criminals day by day. ATM systems today use no more than an access card and PIN for identity verification. This situation is unfortunate since tremendous progress has been made in biometric identification techniques, including finger printing, retina scanning, and facial recognition. This paper proposes the development of a system that integrates facial recognition technology into the identity verification process used in ATMs. The development of such a system would serve to protect consumers and financial institutions alike from fraud and other breaches of security.














1. INTRODUCTION

The rise of technology in India has brought into force many types of equipment that aim at more customer satisfaction. ATM is one such machine which made money transactions easy for customers to bank. The other side of this improvement is the enhancement of the culprit's probability to get his 'unauthentic' share. Traditionally, security is handled by requiring the combination of a physical access card and a PIN or other password in order to access a customer's account. This model invites fraudulent attempts through stolen cards, badly-chosen or automatically assigned PINs, cards with little or no encryption schemes, employees with access to non-encrypted customer account information and other points of failure.
Our paper proposes an automatic teller machine security model that would combine a physical access card, a PIN, and electronic facial recognition. By forcing the ATM to match a live image of a customer's face with an image stored in a bank database that is associated with the account number, the damage to be caused by stolen cards and PINs is effectively neutralized. Only when the PIN matches the account and the live image and stored image match would a user be considered fully verified.
The main issues faced in developing such a model are keeping the time elapsed in the verification process to a negligible amount, allowing for an appropriate level of variation in a customer's face when compared to the database image, and that credit cards which can be used at ATMs to withdraw funds are generally issued by institutions that do not have in-person contact with the customer, and hence no opportunity to acquire a photo.
Because the system would only attempt to match two (and later, a few) discrete images, searching through a large database of possible matching candidates would be unnecessary. The process would effectively become an exercise in pattern matching, which would not require a great deal of time. With appropriate lighting and robust learning software, slight variations could be accounted for in most cases. Further, a positive visual match would cause the live image to be stored in the database so that future transactions would have a broader base from which to compare if the original account image fails to provide a match - thereby decreasing false negatives.
When a match is made with the PIN but not the images, the bank could limit transactions in a manner agreed upon by the customer when the account was opened, and could store the image of the user for later examination by bank officials. In regards to bank employees gaining access to customer PINs for use in fraudulent transactions, this system would likewise reduce that threat to exposure to the low limit imposed by the bank and agreed to by the customer on visually unverifiable transactions.
In the case of credit card use at ATMs, such a verification system would not currently be feasible without creating an overhaul for the entire credit card issuing industry, but it is possible that positive results (read: significant fraud reduction) achieved by this system might motivate such an overhaul.
The last consideration is that consumers may be wary of the privacy concerns raised by maintaining images of customers in a bank database, encrypted or otherwise, due to possible hacking attempts or employee misuse. However, one could argue that having the image compromised by a third party would have far less dire consequences than the account information itself. Furthermore, since nearly all ATMs videotape customers engaging in transactions, it is no broad leap to realize that banks already build an archive of their customer images, even if they are not necessarily grouped with account information.


2. LITERATURE REVIEW

For most of the past ten years, the majority of ATMs used worldwide ran under IBM's now-defunct OS/2. However, IBM hasn't issued a major update to the operating system in over six years. Movement in the banking world is now going in two directions: Windows and Linux. NCR, a leading world-wide ATM manufacturer, recently announced an agreement to use Windows XP Embedded in its next generation of personalized ATMs (crmdaily.com.) Windows XP Embedded allows OEMs to pick and choose from the thousands of components that make up Windows XP Professional, including integrated multimedia, networking and database management functionality. This makes the use of off-the-shelf facial recognition code more desirable because it could easily be compiled for the Windows XP environment and the networking and database tools will already be in place.
For less powerful ATMs, KAL, a software development company based in Scotland, provides Kalignite CE, which is a modification of the Windows CE platform. This allows developers that target older machines to more easily develop complex user-interaction systems . Many financial institutions are relying on a third choice, Windows NT, because of its stability and maturity as a platform.
On an alternative front, the largest bank in the south of Brazil, Banrisul, has installed a custom version of Linux in its set of two thousand ATMs, replacing legacy MS-DOS systems. The ATMs send database requests to bank servers which do the bulk of transaction processing (linux.org.) This model would also work well for the proposed system if the ATMs processors were not powerful enough to quickly perform the facial recognition algorithms.
In terms of the improvement of security standards, MasterCard is spearheading an effort to heighten the encryption used at ATMs. For the past few decades, many machines have used the Data Encryption Standard developed by IBM in the mid 1970s that uses a 56-bit key. DES has been shown to be rather easily cracked, however, given proper computing hardware. In recent years, a "Triple DES" scheme has been put forth that uses three such keys, for an effective 168-bit key length. MasterCard now requires new or relocated ATMs to use the Triple DES scheme, and by April, 2005, both Visa and MasterCard will require that any ATM that supports their cards must use Triple DES. ATM manufacturers are now developing newer models that support Triple DES natively; such redesigns may make them more amenable to also including snapshot cameras and facial recognition software, more so than they would be in regards to retrofitting pre-existing machines .
There are hundreds of proposed and actual implementations of facial recognition technology from all manner of vendors for all manner of uses. However, for the model proposed in this paper, we are interested only in the process of facial verification - matching a live image to a predefined image to verify a claim of identity - not in the process of facial evaluation - matching a live image to any image in a database. Further, the environmental conditions under which the verification takes place - the lighting, the imaging system, the image profile, and the processing environment - would all be controlled within certain narrow limits, making hugely robust software unnecessary .One leading facial recognition algorithm class is called image template based. This method attempts to capture global features of facial images into facial templates. Neural networks, among other methods, are often used to construct these templates for later matching use. An alternative method, called geometry-based, is to explicitly examine the individual features of a face and the geometrical relationship between those features (Gross.) What must be taken into account, though, are certain key factors that may change across live images: illumination, expression, and pose (profile.)
A study was recently conducted of leading recognition algorithms, notably one developed by two researchers at MIT, Baback Moghaddam and Alex Pentland, and one a commercial product from Identix called Facelt. The MIT program is based on Principal Feature Analysis, an adaptation of template based recognition. FaceIt's approach uses geometry-based local feature analysis. Both algorithms have to be initialized by providing the locations of the eyes in the database image, from which they can create an internal representation of the normalized face. It is this representation to which future live images will be compared .
In the study, it was found that both programs handled changes in illumination well. This is important because ATM use occurs day and night, with or without artificial illumination. Likewise, the programs allowed general expression changes while maintaining matching success. However, extreme expressions, such as a scream profile, or squinted eyes, dropped the recognition rates significantly. Lastly, matching profile changes worked reasonably well when the initial training image(s) were frontal, which allowed 70-80% success rates for up to 45 degrees of profile change... however, 70-80% success isn't amenable to keeping ATM users content with the system.
The natural conclusion to draw, then, is to take a frontal image for the bank database, and to provide a prompt to the user, verbal or otherwise, to face the camera directly when the ATM verification process is to begin, so as to avoid the need to account for profile changes. With this and other accommodations, recognition rates for verification can rise above 90%. Also worth noting is that FaceIt's local feature analysis method handled variations in the test cases slightly better than the PGA system used by the MIT researchers .
Another paper shows more advantages in using local feature analysis systems. For internal representations of faces, LFA stores them topographically; that is, it maintains feature relationships explicitly. Template based systems, such as PGA, do not. The advantages of LFA are that analysis can be done on varying levels of object grouping, and that analysis methods can be independent of the topography. In other words, a system can examine just the eyes, or the eyes nose and mouth, or ears, nose, mouth and eyebrows, and so on, and that as better analysis algorithms are developed, they can fit within the data framework provided by LFA
The conclusion to be drawn for this project, then, is that facial verification software is currently up to the task of providing high match rates for use in ATM transactions. What remains is to find an appropriate open-source local feature analysis facial verification program that can be used on a variety of platforms, including embedded processors, and to determine behavior protocols for the match / non-match cases.


3. OUR METHODOLOGY
The first and most important step of this project will be to locate a powerful open-source facial recognition program that uses local feature analysis and that is targeted at facial verification. This program should be compilable on multiple systems, including Linux and Windows variants, and should be customizable to the extent of allowing for variations in processing power of the machines onto which it would be deployed.
We will then need to familiarize ourselves with the internal workings of the program so that we can learn its strengths and limitations. Simple testing of this program will also need to occur so that we could evaluate its effectiveness. Several sample images will be taken of several individuals to be used as test cases - one each for "account" images, and several each for "live" images, each of which would vary pose, lighting conditions, and expressions.
Once a final program is chosen, we will develop a simple ATM black box program. This program will server as the theoretical ATM with which the facial recognition software will interact. It will take in a name and password, and then look in a folder for an image that is associated with that name. It will then take in an image from a separate folder of "live" images and use the facial recognition program to generate a match level between the two. Finally it will use the match level to decide whether or not to allow "access", at which point it will terminate. All of this will be necessary, of course, because we will not have access to an actual ATM or its software.
Both pieces of software will be compiled and run on a Windows XP and a Linux system. Once they are both functioning properly, they will be tweaked as much as possible to increase performance (decreasing the time spent matching) and to decrease memory footprint.
Following that, the black boxes will be broken into two components - a server and a client - to be used in a two-machine network. The client code will act as a user interface, passing all input data to the server code, which will handle the calls to the facial recognition software, further reducing the memory footprint and processor load required on the client end. In this sense, the thin client architecture of many ATMs will be emulated.
We will then investigate the process of using the black box program to control a USB camera attached to the computer to avoid the use of the folder of "live" images. Lastly, it may be possible to add some sort of DES encryption to the client end to encrypt the input data and decrypt the output data from the server - knowing that this will increase the processor load, but better allowing us to gauge the time it takes to process.

4. CONCLUSION
We thus develop an ATM model that is more reliable in providing security by using facial recognition software. By keeping the time elapsed in the verification process to a negligible amount we even try to maintain the efficiency of this ATM system to a greater degree.

Send me ieee formate of an atm with an eye

please send me a ppt on an atm with an eye

please send me ieee paper on an atm with an eyes and ppt to my email id (kanakae777[at]gmail.com)

To get full information or details of atm with an eye please have a look on the pages

http://studentbank.in/report-atm-with-an-eye

http://studentbank.in/report-atm-with-an...4#pid11994

if you again feel trouble on atm with an eye please reply in that page and ask specific fields in atm with an eye

send this copy to padmaadla671[at]gmail.com