Research Paper on ATM Based on Face Recognitions System

Abstract

Automated Teller Machines are widely used nowadays by people. But it’s hard to carry their ATM card everywhere, people may forget to have their ATM card or forget their PIN number. The ATM card may get damaged and users can have a situation where they can’t get access to their money. In our proposal, use of biometrics for authentication instead of PIN and ATM card is encouraged. Here, The Face ID is preferred to high priority, as the combination of these biometrics proved to be the best among the identification and verification techniques. The implementation of ATM machines comes with the issue of being accessed by illegitimate users with valid authentication code. The users are verified by comparing the image taken in front of the ATM machine, to the images which are present in the. If the user is legitimate the new image is used to train the model for further accuracy. This system uses open CV to process the image being obtained and Hear Cascade Classifier to detect the faces in the image. The face recognitions done using Local Binary Pattern.

Introduction

I. INTRODUCTION

An Automatic Teller Machine (ATM) is a computerized machine that is used to withdraw cash from a customer’s respective bank account. As financial users prefer ATM for cash withdrawals, cash deposits and much other transaction, the banks are focusing a lot over the security of ATMs. Hence ATM should be protected properly from the criminal activities or from any unwanted things.

Due to rapid development in science and technology, upcoming innovations are being built- up with strong security. But on the other hand, threats are also being posed to destroy this security level. Though enhancement in automation has made a positive impact overall, various financial institutions like banks and applications like ATM are still subjected to thefts and frauds. The existing ATM model uses a card and a PIN which gives rise to increase in attacks in the form of stolen cards, or due to statically assigned PINs, duplicity of cards and various other threats. Then another major problem is hacking of PIN. There are other fraudulent attacks like eavesdropping, spoofing, brute force attacks, blackmailing the user. An Automatic Teller Machine (ATM) is a computerized machine that is used to withdraw cash from a customer’s respective bank account. As financialusers prefer ATM for cash withdrawals, cash deposits and much other transaction, the banks are focusing a lot over the security of ATMs. Hence ATM should be protected properly from the criminal activities or from any unwanted things.

Due to rapid development in science and technology, upcoming innovations are being built- up with strong security. But on the other hand, threats are also being posed to destroy this security level. Though enhancement in automation has made a positive impact overall, various financial institutions like banks and applications like ATM are still subjected to thefts and frauds. The existing ATM model uses a card and a PIN which gives rise to increase in attacks in the form of stolen cards, or due to statically assigned PINs, duplicity of cards and various other threats. Then another major problem is hacking of PIN.

To overcome these problems, the project ’ATM Security system based on Face recognition, PIN and PIN’ consists of conventional features i.e. is Personal Identification Number (PIN) along with additional features like face recognition and one-time password (PIN) is used. Database holds information about a user’s ac- count details, images of his/her face and a mobile number which will improve security to a large extent.

First, the user will come to the ATM machine and a live image is captured through the Web Camera interfaced with System defining as the ATM system, which is compared with the images stored in the database. If the face is recognized, then the user is notified to type the PIN. If the PIN matches, an PIN will be sent to the corresponding registered mobile number. If the user correctly enters the PIN, the transaction can proceed. Therefore, the combination of face recognition algorithm, PIN and an PIN drastically reduces the chances of fraud.

II. LITERATURE REVIEW

1. Paper Name: An Introduction to the Good, the Bad, and the Ugly Face Recognition Challenge Problem.

Author: P. Jonathon Phillips, J. Ross Beveridge, Bruce A. Draper, Geof Givens, Alice J. O’Toole, David S. Bolme, Joseph Dunlop, Yui Man Lui, Hassan Sahibzada, and Samuel Weimer

Abstract: This paper introduces the Good, the Bad, and the Ugly Challenge Problem. The main goal of the challenge is to encourage the development of algorithms that are robust to recognizing frontal faces taken outside of studio style image collections. The three partitions in the GBU Challenge Problem emphasize the range of performance that is possible when comparing faces photographed under these conditions. This structure allows for researchers to concentrate on the hard aspects of the problem while not compromising performance on the easier aspects.

2. Paper Name: Deep Face: Closing the Gap to Human-Level Performance in Face Verification

Author: Yaniv Taigman Ming Yang MarcAurelio Ranzato Face- book AI Research Menlo Park, CA, USA yaniv, mingyang, ranzato@fb.com Lior Wolf

Abstract: This paper, demonstrates that coupling a 3D model- based alignment with large capacity feed forward models can effectively learn from many examples to Overcome the drawbacks and limitations of previous methods. The ability to present a marked improvement in face recognition, attests to the potential of such coupling to become significant in other vision domains as well.

3. Paper Name: Toward a Practical Face Recognition System: Robust Alignment and Illumination by Sparse Representation

Author: Andrew Wagner, Student Member, IEEE, John Wright, Member, IEEE, Arvind Ganesh, Student Member, IEEE, Zihan Zhou, Student Member, IEEE, Hossein Mobahi, and Yi Ma, Senior Member, IEEE

Abstract: In this paper, the system achieves extremely stable performance under a wide range of variations in illumination, misalignment, and even under small amounts of pose and occlusion. We achieve very good recognition performance on large-scale tests with public data sets as well as our practical face images, while using only frontal 2D images in the gallery and no explicit 3D face model. Our system could potentially be extended to better handle large pose and expression, either by incorporating training images with different poses or expressions or by explicitly modeling and compensating the associated deformations in the alignment stage.

III. PROPOSED SYSTEM

1. Motivation: Motivation is to provide the latest review of the existing literature on facial recognition and to bring to the limelight, the studies of computer vision in recognition of human faces. A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image.
2. The goal of Object: detection is to detect all instances of objects from known class, such as people, cars or faces in an image. Typically, only a small number of instances of the object are present in the image, but there is a very large number of possible locations and scales at which they can occur and that need to somehow be explored.
3. Problem Definition: Now a days ATM card are easy to Hack and get fraud. So because of that we are developing a face unlocking transaction system in this we are using image processing. so user face is first capture their own image then system store it into data set after that training of model operation is performed with the help of ML algorithm and give permission to do transaction.
4. Objectives: The goal of object detection is to detect all instances of objects from a known class, such as people, cars or faces in an image. Typically, only a small number of instances of the object are present in the image, but there is a very large number of possible locations and scales at which they can occur and that need to somehow be explored. Each detection is reported with some form of pose information. This could be as simple as the location of the object, a location and scale, or the extent of the object defined in terms of a bounding box.
5. Scope of the Work: To study the effects of age in a face biometric system and to review face biometric technology available on PDAs and laptops. So it is helpful for future scope as it will Recognize face at different times and it will helpful as the age increases of a person there are changes in face of a person so it detects the face at specific age.

IV. METHODOLOGY

Hear Cascade Hear Cascade is an Object Detection Algorithm used to identify faces in an image or a real time video.

The algorithm uses edge or line detection features proposed by Viola. Hear Cascade is a machine learning-based approach where a lot of positive and negative images are used to train the classifier.

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This algorithm considers the fact that not all parts of a face are equally important or useful for face recognition. Indeed, when you look at someone, you recognize that person by his distinct features, like the eyes, nose, cheeks or fore- head; and how they vary respect to each other. Focus is on the areas of maximum change. For example, from the eyes to the nose there is a significant change, and same applies from the nose to the mouth. When multiple faces are given, comparison is done by looking at these areas, because by catching the maximum variation among faces, they help to differentiate one face from the other. This is how Eigen faces recognizer works. It looks at all the training images of all the people as a whole and tries to extract the components which are relevant and useful and discards the rest.

V. IMPLEMENTATION PLAN

A. Model Training

Train the face recognition model using the labeled dataset. This typically involves optimizing the model's parameters to minimize the difference between the predicted face embeddings and the ground truth labels

B. Model Testing and Evaluation

Evaluate the trained model's performance on a separate test dataset to measure its accuracy, precision, recall, and other relevant metrics.

1. Front End: HTML, CSS
2. Backend: Python, Django Framework
3. Framework: Spyder
4. Algorithms: Haar Cascade

C. Graph

Conclusion

1) This project focuses on developing an automated system for ATM, and security purpose. 2) In this project we are developing a system in this system user face detect and then system give permission to user for complete transaction or next process. 3) The motive of our system is card less transaction.so there is not card we are using user face for authentication.so reduce the robbery of ATM card money. Through this system, face which are in front of the camera are detected. 4) The distance of the face from that person is calculate. 5) Facial recognition has proven to be one of the most secure methods of all biometric systems to a point for high level security and to avoid ATM robberies and provide security for ATM. 6) It replaces the traditional ATM system. It has advantages such as saves manufacturing cost of cards and overcomes drawbacks of the traditional system like carrying the ATM card, losing of card, fraud calls related to ATM card, etc. 7) With new improved techniques in the field of artificial Intelligence that help eliminate more disturbances and distortions, the rate of effectiveness of the system can be improved.

References

[1] X. Wei, C.-T. Li, Z. Lei, D. Yi, and S. Li, “Dynamic Image-to- Class Warping for Occluded Face Recognition,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 12, pp. 2035–2050, Dec 2014. [2] P. J. Phillips, J. R. Beveridge, B. A. Draper, G. Givens, A. J. O’Toole, D. S. Bolme, J. Dunlop, Y. M. Lui, H. Sahibzada, and S. Weimer, “An introductionto the good, the bad, the ugly face recognition challenge problem,” in 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG). IEEE, 2011, pp. 346–353. [3] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to humanlevel performance in face verification,” in 2014 IEEE Confer- ence on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014, pp. 1701–1708. [4] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recog- nition via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210–227, 2009. [5] A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, “Toward a practical face recognition system: Robust alignment and illumination by sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 2, pp. 372– 386, 2012 [6] Bayly, M., Regan, M., Hosking, S.: ‘Intelligent transport systems and motor-cycle safety’ (Monash University, Accident Research Centre, 2006), p. 260 [7] Bianco, A., Trani, F., Santoro, G., Angelillo, I.F.: ‘Adolescents’ attitudes andbehaviour towards motorcycle helmet use in Italy’, Eur. J. Pediatr., 2005, 164,(4), pp. 207– 211

Copyright

Copyright © 2023 Prof. Arunadevi Khaple, Shlok Kailas Khopkar, Jayesh Datary Kharat, Rutuja Rameshwar Zori, Ruchita Jayawant Kakade. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.