secure atm by image processing


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secure atm by image processing and biometrics


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INTRODUCTION T his paper proposes an ATM security model that would combine a physical access card ,a pin and electronic facial recognition. It encloses the information regarding the ‘ image processing’. And discussed one of the major application of image processing ‘biometrics’. Biometrics technology turns your body in to your password. We discussed various biometric techniques like finger scan, retina scan, facial scan, hand scan etc. . Face recognition technology may solve the problem since a face is undeniably connected to its owner making it impenetrable system.


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).


Proposal Cameras in use at automatic teller machines should take still images of users A facial recognition scheme should be added to the software used to verify users at ATMs This scheme should match a picture of the user at the ATM with a picture of the account holder in the bank’s database


Reasoning ATM fraud costs U.S. banks an average of $15,000 each year… hundreds of millions in total This cost is borne by bank customers Current ATM validation schemes are limited to access cards and PINs Card theft, PIN theft and cracking, stealing of account information by bank employees all contribute to fraud schemes


Algorithm Take customer’s picture(s) when account is opened and allow user to set non-verified transaction limits At ATM, use access card and PIN to pre-verify user Take user’s picture, attempt to match it to database image(s) If match is successful, allow transaction If match is unsuccessful, limit available transactions

Can ATMs Support This?:

Can ATMs Support This? Most current generation ATMs run Windows CE, 2000, XP Embedded, or Linux – these machines can run facial recognition software locally ATM Bank Computer 1: Image, account no, PIN 2: Account no, PIN 3: Bank-held customer image 4: Processing 5: User verified

Can ATMs Support This?:

Can ATMs Support This? Older ATMs run DOS or OS/2 – these machines can offload the processing to the bank’s computers ATM Bank Computer 1: Image, account no, PIN 2: Image, account no, PIN 4: User verified message 5: User verified message 3: Processing


BIOMETRICS A biometric is a unique, measurable characteristic of a human being that can be used to automatically recognize an individual or verify an individual’s identity. Biometrics can measure both physiological and behavioral characteristics. Physiological biometrics -based on measurements and data derived from direct measurement of a part of the human body. Behavioral biometrics -based on measurements and data derived from an action.


HOW BIOMETRICS WORKS In biometrics a series of steps are followed to get the aimed goal, the steps are as shown in the figure below : Sensor : A sensor collects data and converts the information to a digital format. Signal processing algorithms : This is where quality control activities and development of the template takes place. Data Storage : Keeps information that new biometric templates will be compared to. Matching algorithm : Compares the new template to other templates in the data storage. Decision process: Uses the results from the matching component to make a system level decision.




TYPES OF BIOMETRICS Finger scan: Finger-scan biometrics is based on the distinctive characteristics of the human fingerprint. Fingerprints are used in forensic applications: large- scale, one-to-many searches on databases of up to millions of fingerprints Retina scan: Retina scan requires the user to situate his or her eye with ½ inch of the capture device and hold still while the reader ascertains the patterns. Retina scan is designed to use in military facilities, logical security applications such as network access or PC logic

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Iris scan : The iris has colored streaks and lines that radiate out from the pupil of the eye. The iris provides the most comprehensive biometric data after DNA. The iris has more unique information than any other single organ in the body. Hand Geometry :This is one of the first succesful commercial biometric products .A person places their hand on a device and the system takes a picture of the hand using mirrors ,then measures digits of the hand and compares to those collected at enrollment .


INITIAL ANALYSIS Out of all the biometric modalities I have chosen FACIAL RECOGNITION as the best technique for my project .This is because of the many algorithms this can make it more secure and more easy to use.In the current state facial recognition is used in high-level national security by the FBI,CIA and the SECRET SERVICES in the United States of America.

Is Facial Recognition Reliable?:

Is Facial Recognition Reliable? It requires no physical interaction on behalf of the user. It is accurate and allows for high enrollment and verification rates. It does not require an expert to interpret the comparison result. It can use your existing hardware infrastructure, existing cameras and image capture Devices will work with no problems It is the only biometric that allow you to perform passive identification.

What Variables Affect Verification?:

What Variables Affect Verification? Lighting Angle of view Extreme facial expressions Facial hair Glasses Please follow these guidelines to help us verify your identity: Face the camera, holding still until you hear the beep Maintain a normal facial expression If you are wearing glasses, please remove them


LOCAL FEATURE ANALYSIS Local feature analysis selects features in each face that differ most from other faces such as, the nose, eyebrows, mouth and the areas where the curvature of the bones changes . Features


IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY The implementation of face recognition technology includes the following four stages : Data acquisition Input processing Face image classification Decision making


DATA ACQUISITION The input can be recorded video of the speaker or a still image. A sample of 1 sec duration consists of a 25 frame video sequence. More than one camera can be used to produce a 3D representation of the face and to protect against the usage of photographs to gain unauthorized access.


INPUT PROCESSING A pre-processing module locates the eye position and takes care of the surrounding lighting condition and colour variance. First the presence of faces or face in a scene must be detected. Once the face is detected, it must be localized and Normalization process may be required to bring the dimensions of the live facial sample in alignment with the one on the template.


FACE IMAGE CLASSIFICATION The appearance of the face can change considerably during speech and due to facial expressions. In particular the mouth is subjected to fundamental changes but is also very important source for discriminating faces. So an approach to person’s recognition is developed based on patio- temporal modeling of features extracted from talking face. Models are trained specific to a person’s speech articulate and the way that the person speaks .


DECISION MAKING Face recognition starts with a picture, attempting to find a person in the image.The face recognition system locates the head and finally the eyes of the individual. A matrix is then developed based on the characteristics of the Individual’s face. The method of defining the matrix varies according to the algorithm This matrix is then compared to matrices that are in a database and a similarity score is generated for each comparison


WHAT WOULD I ACTUALLY DO? Find open-source Local Features Algorithm recognition programs compilable on multiple systems including Linux and Windows. Develop an ATM black box module. Create two databases of images. Tweak and test recognition programs with ATM module and images. Rewrite ATM module into client/server version with encryption to emulate ATM/bank interactions. Add USB camera control to client Possibly add some sort of DES encryption.


CONCLUSION With new improved techniques like ARTIFICIAL INTELLIGENCE security margin can be increased from simple 60-75% to 80-100% 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 making it faster and impenetrable.

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