cardless atm implementation as a project

Cardless atm implementation

1. Geetha Shishu Shikshana Sangha(R) GSSS INSTITUTE OF ENGINEERING & TECHNOLOGY FOR WOMEN (Affiliated to VTU, Belagavi, Approved by AICTE, New Delhi & Govt. of Karnataka) K.R.S Road, Metagalli , Mysuru-570016 Accredited with ‘A’ grade by NAAC DEPARTMENT OF INFORMATION SCIENCE AND ENGINEERING Accredited by NBA, New Delhi, (Validity: 01.07.2023 to 30.06.2026 ) MINI PROJECT SYNOPSIS [21ISMP67] ON “CARDLESS ATM TRANSACTION USING FACE RECOGNITION AND FINGERPRINT“ NAVYA H G (4GW21IS027) under the guidance of RACHANA S (4GW21IS038) Anand M SHALINI N (4GW21IS037) Assistant Professor, Dept. Of ISE

2. contents • Introduction • Aim and Objectives • Justification of the project • Related work (Literature review) • System Requirements: a. Hardware Requirements b. Software Requirements • Proposed Method (Methodology) • Expected outcomes of the project • References

3. The advancement of biometric technology has revolutionized the banking sector, offering innovative and secure methods for accessing financial services. This technology eliminates the need for physical cards, thereby reducing the risk of card theft, loss, and cloning. Introduction

4. AIM AND objectives To design a secure backend infrastructure that supports real-time biometric authentication and transaction processing. To develop a biometric-based authentication system for cardless ATM transactions to enhance security and user convenience . The system aims to increase accessibility for all users, ensure high reliability with minimal errors, and promote wide spread adoption The aim is to develop a secure and efficient cardless ATM transaction system using face recognition and fingerprint authentication

5. Justification of the project Biometric authentication, using face and fingerprint recognition, offers a higher level of security compared to traditional card-based systems. Cardless ATM transactions eliminate the need for users to carry physical cards, reducing the risk of card loss or damage. By transitioning to a cardless system, banks can reduce costs associated with the production, distribution, and maintenance of physical cards.

6. Literature survey Title Author(s) References Description Limitations Year “A Secure Cardless ATM System Using Face and Fingerprint Recognition" Smith J, Kumar A, Zhang L. International Journal of Security, 12(3), 45-56. This paper proposes a cardless ATM system that uses both face recognition and fingerprint scanning for authentication. High implementation cost and potential privacy concerns. 2019 "Biometric Authentication in ATMs: Face and Fingerprint Recognition" Chen Y, Lee M, PatelR . Journal of Financial Technology, 8(2), 99-113. Includes algorithms and hardware requirements, and provide a comparative analysis with traditional card based systems. Technical complexity and the need for extensive user data collection. 2020 Table1. Literature Survey

7. "Enhancing ATM Security with Cardless Transaction Using Biometric Technology" Ahmed S, Wang H, Diaz P . Computer Science Review,14(4), 211-225. This paper presents comprehensive review of biometric technologies for cardless ATM transactions. Issues with biometric data storage and management 2021 " Cardless ATM Access: A Study on Face and Fingerprint Recognition " Brown, T, Silva, E., Gupta, N Transactions on Information Forensics, 15(1), 34- 48. The authors conduct experiments to evaluate the system's accuracy, speed, and user satisfaction. Requires high-quality sensors and consistent lighting conditions for face recognition. 2022 “ Future of ATM Transactions: Biometric Authentication Using Face and Fingerprint Scans” Lopez, R., Khan, I., Tan,W . Biometric Authentication Using Face and Fingerprint Scans. Future Banking Journal, 10(2),123- 137. This article explores the future prospects of ATM transactions with the adoption of biometric authentication methods. Privacy issues and the potential for biometric spoofing. 2023 Table1. Literature Survey

8. a. Hardware requirements: • Processor : Intel i3 • RAM : 500GB • Hard disk : 4GB b. Software requirements: • Operating System: Windows 7 and above • Front End: Html, CSS • Framework: Flask • Language: Python version3.7 • Libraries: Pandas, Numpy , Sklearn , Scikit • Editor: Jupyter Required specifications

9. This project follows a multi-stage approach to integrate biometric authentication and secure transaction processing, creating a comprehensive cardless ATM transaction system. To optimize route processing and enhance transaction efficiency, algorithms such as Dijkstra's and A* are employed. Proposed Method (Methodology)

10. An advanced development of a biometric-based authentication system for ATMs, significantly enhancing security and convenience for users. Expected Outcomes of the Project Increased accessibility through a seamless and user-friendly ATM interface through facial recognition and fingerprint scanning technology.

11. 1. "A Secure Cardless ATM System Using Face and Fingerprint Recognition", Smith, J, Kumar A, Zhang L, 2019. 2. "Biometric Authevntication in ATMs: Face and Fingerprint Recognition", Chen Y, Lee M, Patel R, International Conference ICT For Smart Society (ICISS), 2020 . 3. "Enhancing ATM Security with Cardless Transaction Using Biometric Technology", Cardless Transactions Using Biometric Technologies Computer Science Review, 14(4), 2021. 4 “Card-less ATM Transaction Using Biometric and Face Recognition”– A N Chirag, 2020.Brown, T, Silva, E., Gupta, N,vol 3696, 2022 . 5. “Future of ATM Transactions: Biometric Authentication Using Face and Fingerprint Scans, vol 586. Springer, Berlin, Heidelberg. Cole S.A, Lopez, R, Khan, I., Tan, 2023. references

12. 6. " Cardless ATM System Using Hybrid Biometric Authentication: Face and Fingerprint", William A, Huan J, Patel S, 4th International Conference on, pp. 86-91, 2022 7. "Implementation of Cardless ATM Transactions Using Mobile Technology“, Gupta, A., & Yadav, S. (2018). 8. Chung-Hua Chu, Yu-Kai Feng: Published Year 01 March 2018 Study of eye blinking to improve face recognition for screen unlock on mobile devices DO - 10.5370/JEET.2018.13.2.953. 9. Bhavesh Pandya, Georgina Cosma , Ali A. Alani, Aboozar Taherkhani , Vinayak Bharadi , T.M McGinnity , "Fingerprint classification using a deep convolutional neural network", Information Management (ICIM) 2018 4th International Conference on, pp. 86-91, 2019. 10. https://youtu.be/vshuuOGHYoI?si=z6uvmJjsUHaUxpoY

13. Thank you