There is an urgent need for improving security in banking region. With the advent of atm though banking became a lot easier it become a lot vunerable . The chances of misuse of this much hyped insecure baby product (atm) are manifold due to the exponention growth of intelligent criminals day by day INTRODUCTION: This paper process an atm insecurity model that would combine a physical access card, apin and electronic facial recognition It encloses the information regarding the image processing .
and discussed one of the major application of image processing in biometrics . bio metrics technology turns your body into 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 impenetrable system.
An auto mated teller machine (ATM) is a computerized tel communications device that provides the customer4s of a finantial transcations in public space with out the need of a human clerk On most modern ATMs, the customer is identified by interesting of 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 expiratiom date or cvv. Security is provided by the customer entering a personal identification number.
The Term Paper on Atm Security – Importance Of Atm Security
Introduction Nowadays people prefer a faster way to access their bank accounts. So that they would not spend time waiting in a line in the bank. It would be a great hindrance in their fast moving life style. This is where ATMs are very important. “People use ATMs without even thinking twice. But not all of them think about the security risks involved. The use of ATM is on a rise and so is the ...
Cameras in use at automatic teller machines should take stil images of users A facial recognition scheme should be added to the software used to verify the users at ATMs This scheme should match a picture of 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. Hunderads of million 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 customers picture when account is opened and allow user to set non-verified transcation limits At ATM, use access card and PIN to pre-verify user Take user’s picture , attempt to match it to database images If match is successful, allow transaction If match is unsuccessful, limit available transactions ATM SUPPORTING BIOMETRICS: Most current generation ATMs run windows CE, 2000, XP embedded, or LINUX- these machines can run facial recognition software locally BIOMETRICS:
A biometric is a unique , measurable characteristics of a human being that can be used to automatically recognize an individual or verify an individual’s identity Biometrics can measure both phsycological 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 fram an action BIOMETRIC WORKING: In biometrics a series of steps are followed to get aimed goal, the steps are shown in the figure below.
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 the 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. DECISION PROCESS: uses the results from the matching component to make a system level.
Professional,ethical and moral issues faced by ICT users
ABSTRACT This report is a study of computer ethics, morals and professional issues facing Information Communication Technology (ICT) users and its relevance to today’s society at large. This issues do not only face ICT users only but the world at large, because it may have effect positively and negatively. This report aims to build knowledge or enhance the understanding about ICT and its ethics, ...
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 ? inch of the capture device and hold still while the reader ascertains the patterns. APPLICATIONS: Retina scan is designed to use in military facilities, logical security applications such as network access or PC logic 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 .
If you are wearing the glasses, please remove them IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY The implementation of face recognition technology includes the following four stages : i)Data acquisition ii) Input processing iii) Face image classification iv)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.
The Dissertation on Video-Based Framework for Face Recognition in Video
National Research Council Canada Institute for Information Technology Conseil national de recherches Canada Institut de technologie de l'information Video-Based Framework for Face Recognition in Video * Gorodnichy, D. May 2005 * published at Second Workshop on Face Processing in Video (FPiV'05) in Proceedings of Second Canadian Conference on Computer and Robot Vision (CRV'05). pp. 330-338. ...
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 the open-source local features algorithm recognition programs compatible 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 I NTELLIGENCE 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 impenertrable
