Gesture Vault: Revolutionizing ATMs with Touchless Technology

doi:10.23940/ijpe.24.07.p1.413420

Abstract

Abstract: Automated Teller Machines (ATMs) are widely used for cash withdrawals and other banking transactions. However, their touchscreens pose a significant health risk due to potential contamination with various pathogens. Studies have shown that ATMs can harbor bacteria and viruses linked to diseases like influenza (25%), E. coli (18%), and Staphylococcus aureus (14%). This research proposes Gesture Vault, a touchless ATM system that leverages facial and hand gesture recognition technologies. Gesture Vault addresses hygiene concerns by eliminating the need for physical contact with the ATM screen. Facial recognition ensures secure user identification, while hand gestures provide an intuitive interface for account selection and transaction initiation. This type of authentication also strengthens security compared to traditional PIN-based systems. Gesture Vault promotes a hygienic and secure banking experience, potentially reducing the spread of infectious diseases and improving user confidence in using ATMs.

Key words: touchless ATM, gesture recognition, facial recognition, ATM security, ATM hygiene

Mohan Krishnan O, Srinithi J, Sri Harshitha P, Ilamughi M, and Antony Seba P. Gesture Vault: Revolutionizing ATMs with Touchless Technology [J]. Int J Performability Eng, 2024, 20(7): 413-420.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

[1] Sadaphule P.S., Khadse A., Amale S. and Chanda V.S., 2017. Hand Gestures Recognition for ATM Machine Interface Using k-NN Algorithm. International Journal,7(5).
[2] Damindarov, R., Fam, C.A., Boby, R.A., Fahim, M., Klimchik, A. and Matsumaru, T., 2021, August. A depth camera-based system to enable touch-less interaction using hand gestures. In 2021 International Conference" Nonlinearity, Information and Robotics"(NIR) (pp. 1-7). IEEE.
[3] Darwin Nesakumar A., Suresh T., Nivedha T., Nivedha K., Priyadharshini G. and Mugilan P., 2020. Smart ATM Security Using Face Recognition.European Journal of Molecular & Clinical Medicine.
[4] Yasen, M. and Jusoh, S., 2019. A systematic review on hand gesture recognition techniques, challenges and applications. PeerJ Computer Science, 5, p.e218.
[5] Sarkar A.R., Sanyal G. and Majumder S.J.I.J.O.C.A., 2013. Hand gesture recognition systems: a survey. International Journal of Computer Applications,71(15).
[6] Gunasundari B, Sreenithi G, Soundarya V, Renugadevi J, 2023. Touchless ATM.International Advanced Research Journal in Science, Engineering, and Technology, 10(6).
[7] Setiawan A., Sigit R. and Rokhana R., 2023. Face Recognition Using Convolution Neural Network Method with Discrete Cosine Transform Image for Login System. JOIV: International Journal on Informatics Visualization,7(2), pp.502-510.
[8] Kittler, J. and Nixon, M.S. eds., 2003. Audio-and Video-Based Biometric Person Authentication: 4th International Conference, AVBPA 2003, Guildford, UK, June 9-11, 2003, Proceedings (Vol. 2688). Springer Science & Business Media.
[9] Xavier S., Vaisakh B. and Pai M.L., 2023, July. Real-time Hand Gesture Recognition Using MediaPipe and Artificial Neural Networks. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)(pp. 1-6). IEEE.
[10] Zhang F., Bazarevsky V., Vakunov A., Tkachenka A., Sung G., Chang C.L. and Grundmann M., 2020. Mediapipe hands: On-device real-time hand tracking.arXiv preprint arXiv:2006.10214.