ATM SECURITY USING FACE RECOGNITION PDF

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Vol-4 Issues 09, September-2020 ISSN: 2456-9348 Impact Factor: 4.520 International Journal of Engineering Technology Research & Management ATM SECURITY USING FACE RECOGNITION Renuka devi .P1, Maheshwari .R.G2, Rekha shree .S3 1 Associate Professor, Department of computer science and Engineering,...

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Vol-4 Issues 09, September-2020

ISSN: 2456-9348 Impact Factor: 4.520

International Journal of Engineering Technology Research & Management

1

ATM SECURITY USING FACE RECOGNITION Renuka devi .P1, Maheshwari .R.G2, Rekha shree .S3

Associate Professor, Department of computer science and Engineering, Paavai Engineering College,Namakkal. 2,3 U.G Students, Department of computer science and Enigneering, Paavai Engineering College, Namakkal [email protected] [email protected] [email protected] ABSTRACT Now a days everyone where using ATM method for their money transaction, withdrawal, deposit. This system is mainly used for secured purposed and to take a money at any time with the help of survillnce with safe manner. There are several bank sector for money usage. Though using this method there is so issue on withdrawal of money. We know the PIN anybody can use the card. So that’s the issue on today’s world. We introduce face recognition to access by only particular members. By using you can use one the card authorized person, join account with someone whom you want to access for your card. This method can also used by twins with some dissimilarity. KEYWORDS: Face recognition,withdrawal,ATM transaction,security,PIN theft, Case Study INTRODUCTION In the era there are plenty of members using bank sector to their own purpose. The technology may develop all the sources of today’s needs. But it has both the advantages and disadvantages of current trend. This method is very effective compare to fingerprint model, because it has problem on facing at old age. Face recognition identifies your face and iris and shape of your nose and mouth, which can easily detects your identity. If any unknown person uses an card cannot be matched, even if they know the PIN of the card. The digital image can uses to analyze and send to your account to withdrawal of money. It will the database to find a match. This technique used facial appearance like contour of eyes, nose, chin, lips. This will store details of faces also. This method is used for adults only. There are two technique method involved for this method.The camera also does not use any kind of beam. Instead, a special lens has been developed will not only blow up the image of the iris, but provide more detail when it does. Iris scans are more accurate than other high-tech id system available that scan voices and fingerprints.Easier transformation of images to any zone. That can be convertional method.In 2D dimension method there is an unique method, which was original and individual method. Here the faces are seen as 2d method the front view of a face and side view of your face. It show the width of the nose lips. This digital image is captured for detecting the faces. Change in facial expression or difference in ambient lighting on an appearance that is not directly looking into the camera. OBJECTIVES Face recognition finds its application in a variety of fields such as homeland security, criminal identification, human-computer interaction, privacy security, etc. The face recognition feature inhibits access of account through stolen or fake cards. The card itself is not enough to access account as it requires the person as well for the transaction to proceed. Eigen face based method is used for the face recognition. However, the drawback of using eigen face based method . sometimes be spoofed by the means of fake masks or photos of

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Vol-4 Issues 09, September-2020

ISSN: 2456-9348 Impact Factor: 4.520

International Journal of Engineering Technology Research & Management an account older. To overcome this problem 3D face recognition methods can be used. One-Time passwords (OTP),OTP ensures that the user is authentic by sending the randomly generated 6-digit code to the registered mobile number of the corresponding account number. METHODOLOGY The first and foremost important step of this system will be to locate a powerful open source facial recognition program that uses local feature analysis and that is targeted at facial verification. Various facial recognition algorithms be familiar with faces by extracting features, from a snap of the subject's face. For ex, an algorithm may examine the size, relative position, in addition to/or outline of the nose, eyes, cheekbone and jaw. These facial appearances are then used to search for other imagery across matching features. Other algorithm manages a balcony of face images and then compresses the images face information and it saves only the data in the image that is used for face detection. RESULTS AND DISCUSSION In spite of all these security features, a new technology has been developed. Bank United of Texas became the first in the United States to offer iris recognition technology at automatic teller machines, providing the customers a card less, password-free way to get their money out of an ATM. a.Iris recognition:There no card to show, there's no fingers to ink, no customer inconvenience or discomfort. It'sjust a photograph of a Bank United customer's eyes. Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of one or both the issues of an individual eyes whose complex patterns. b.Biometric technique:The biometric system is used to help registration officers to improve the accuracy of voter identification. Biometric systems are electronic systems specialized on identifying a user by means of processing unique psychological or behavioural characteristics of the user. C.Face-detector:The face detector spot the face, eliminating any other detail, not related to the face (like the backdrop). It identifies the facial region and leaves the non-facial region in the photo of the person to be d.Eye-localizer: It finds the spot of the eyes; so that the position of the face can be identified. e.Recognizer:It will check the database to find a match ACKNOWLEDGEMENT We thank the staff and our colleagues from the Rural Heath Unit of Jose Abad Santos, Davao Occidental, Philippines, headed by the Municipal Health Officer, Dr. Amparo A. Lachica, who provided insight and expertise that greatly assisted the research. We thank the Graduate School of Government and Management, University of Southeastern Philippines for assistance and for comments that greatly improved the manuscript. We are expressing our gratitude to our families for being an inspiration. Above all, to God. CONCLUSION The facial recognition has proven to be the most secure method of all biometric systems to a point it is widely used in high level security. If this system is used at this level it should show how much technology has changed in order to make this method effective in processes of identification and verification.The biometric ATM system is highly secure as it provides authentication with the information.

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Vol-4 Issues 09, September-2020

ISSN: 2456-9348 Impact Factor: 4.520

International Journal of Engineering Technology Research & Management REFERENCES [1] The New York Times., Apr. 2013, [online] Available: http://www.nytimes.com/2013/ 04/ 19/ us/ fbi-releases-video-of-boston-bombing-suspects.html. [2] FEI Face Database., [online] Available. [3] P. J. Phillips, H. Moon, P. Rauss, S. A. Rizvi, "The FERET evaluation methodology for facerecognition algorithms", Pro6. X. Tan, B. Triggs, "Enhanced local texture feature sets for face recognition under difficult lighting conditions", IEEE Trans. Image Process., vol. 19, no. 6, pp. 16351650, Jun. 2010.c. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 137-143, Jun. 1997. [4] T. Sim, S. Baker, M. Bsat, "The CMU pose illumination and expression (PIE) database", Proc. 5th IEEE Int. Conf. Autom. Face Gesture Recognit., pp. 46-51, May 2003. [5] P. Viola, M. J. Jones, "Robust real-time face detection", Int. J. Comput. Vis., vol. 57, no. 2, pp. 137-154, May 2004. [6] Facevacs Software Developer Kit Cognitec Systems GmbH., 2012, [online] Available [7] L. Wiskott, J.-M. Fellous, N. Kuiger, C. von der Malsburg, "Face recognition by elastic bunch graph matching", IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 775-779, Jul. 1997. [8] Faune Hughes Daniel Lighter Richard Oswald Michael Whitfield "Face Biometrics: A Longitudinal Study" Seidenberg School of CSIS Pace University. [9] Gary G. Yen Nethrie Nithianandan "Facial Feature Extraction Using Genetic Algorithm Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering.

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