ATM Access Using Card Scanner and Face Recognition with AIML
Main Article Content
Abstract
In recent years, the increasing number of ATM frauds has highlighted the limitations of traditional card-and-PIN–based authentication systems. To address these security challenges, this project proposes an enhanced ATM access system that combines card scanning technology with AI/ML-based face recognition to ensure secure and reliable user authentication. The system introduces a multi-layered verification mechanism aimed at preventing unauthorized access while maintaining ease of use for legitimate users.The proposed system is implemented using Python, with Tkinter used to design a graphical user interface that simulates ATM operations such as card scanning, authentication status, and transaction access. User card information and facial data references are maintained using CSV files, providing a lightweight and efficient method for structured data storage. File handling tasks, including storing and managing user images and logs, are performed using the os and shutil modules.
The authentication process begins with card verification, followed by AI/ML-based face recognition that compares the live facial image of the user with registered records. The time and datetime modules are utilized to record access timestamps and monitor suspicious activity patterns, such as repeated failed attempts. To strengthen security, random and string modules are used to generate unique session identifiers and temporary access tokens during each transaction.
To ensure smooth operation and real-time responsiveness, threading is employed to run facial recognition and verification processes concurrently without interrupting the GUI. System-level operations and exception handling are managed using the sys module to improve reliability and error control. In cases of suspicious or failed authentication attempts, the system automatically sends security alerts using smtplib and the email module to notify the account holder or bank authority. By integrating card-based verification with biometric authentication powered by AI/ML techniques, the proposed ATM system significantly enhances security, reduces fraud risk, and demonstrates a practical approach to intelligent and secure banking systems.
Article Details
Section
How to Cite
References
1. Prasanna, D., Ahamed, N. A., Abinesh, S., Karthikeyan, G., & Inbatamilan, R. (2024, November). Cloud-based automatically human document authentication processes for secured system. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1–7). IEEE.
2. Sugumar, R. (2025). Explainable AI-driven secure multi-modal analytics for financial fraud detection and cyber-enabled pharmaceutical network analysis. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(6), 13239–13249.
3. Lakshmi, A. J., Dasari, R., Chilukuri, M., Tirumani, Y., Praveena, H. D., & Kumar, A. P. (2023, May). Design and implementation of a smart electric fence built on solar with an automatic irrigation system. In 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (pp. 1553–1558). IEEE.
4. David, A. (2020). Air pollution control monitoring & delivery rate escalated by efficient use of Markov process in MANET networks: To measure quality of service parameters. Test Engineering & Management.
5. Karthikeyan, K., & Umasankar, P. (2025). A novel buck-boost modified series forward (BBMSF) converter for enhanced efficiency in hybrid renewable energy systems. Ain Shams Engineering Journal, 16(10), 103557.
6. Inbavalli, M., & Arasu, T. (2015). Efficient analysis of frequent item set association rule mining methods. International Journal of Scientific & Engineering Research, 6(4).
7. Sammy, F., Chettier, T., Boyina, V., Shingne, H., Saluja, K., Mali, M., ... & Shobana, A. (2025). Deep learning-driven visual analytics framework for next-generation environmental monitoring. Journal of Applied Science and Technology Trends, 114–122.
8. Saravanan, M., Kumar, A. S., Devasaran, R., Seshadri, G., & Sivaganesan, S. (2019). Performance analysis of very sparse matrix converter using indirect space vector modulation. International Journal of Innovative Technology and Exploring Engineering, 9(1), 4756–4762.
9. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder–decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
10. Sakthivel, T. S., Ragupathy, P., & Chinnadurai, N. (2025). Solar system integrated smart grid utilizing hybrid coot-genetic algorithm optimized ANN controller. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 1–24.
11. Ananth, S., Radha, D. K., Prema, D. S., & Nirajan, K. (2019). Fake news detection using convolution neural network in deep learning. International Journal of Innovative Research in Computer and Communication Engineering, 7(1), 49–63.
12. Vani, S., Malathi, P., Ramya, V. J., Sriman, B., Saravanan, M., & Srivel, R. (2024). An efficient black widow optimization-based faster R-CNN for classification of COVID-19 from CT images. Multimedia Systems, 30(2), 108.
13. Poornachandar, T., Latha, A., Nisha, K., Revathi, K., & Sathishkumar, V. E. (2025, September). Cloud-based extreme learning machines for mining waste detoxification efficiency. In 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 1348–1353). IEEE.
14. Kumar, A. S., Saravanan, M., Joshna, N., & Seshadri, G. (2019). Contingency analysis of fault and minimization of power system outage using fuzzy controller. International Journal of Innovative Technology and Exploring Engineering, 9(1), 4111–4115.
15. Dharnasi, P. (2025). A multi-domain AI framework for enterprise agility integrating retail analytics with SAP modernization and secure financial intelligence. International Journal of Humanities and Information Technology, 7(4), 61–66.
16. Prasanna, D., & Manishvarma, R. (2025, February). Skin cancer detection using image classification in deep learning. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1–8). IEEE.
17. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
18. Yashwanth, K., Adithya, N., Sivaraman, R., Janakiraman, S., & Rengarajan, A. (2021, July). Design and development of pipelined computational unit for high-speed processors. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1–5). IEEE.
19. Aashiq Banu, S., Sucharita, M. S., Soundarya, Y. L., Nithya, L., Dhivya, R., & Rengarajan, A. (2020). Robust image encryption in transform domain using duo chaotic maps—A secure communication. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020 (pp. 271–281). Springer.
20. Madheswaran, M., Dhanalakshmi, R., Ramasubramanian, G., Aghalya, S., Raju, S., & Thirumaraiselvan, P. (2024, April). Advancements in immunization management for personalized vaccine scheduling with IoT and machine learning. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 1566–1570). IEEE.
21. Saravanan, M., & Sivakumaran, T. S. (2016). Three phase dual input direct matrix converter for integration of two AC sources from wind turbines. Circuits and Systems, 7, 3807–3817.
22. Ananth, S., Radha, D. K., Prema, D. S., & Nirajan, K. (2019). Fake news detection using convolution neural network in deep learning. International Journal of Innovative Research in Computer and Communication Engineering, 7(1), 49–63.
23. Karthikeyan, K., Umasankar, P., Parathraju, P., Prabha, M., & Pulivarthy, P. Integration and analysis of solar vertical axis wind hybrid energy system using modified zeta converter.