Convolutional Neural Network Method based Security Solution for Facial Recognition in ATM

DOI

10.2991/978-94-6463-754-0_11How to use a DOI?

Keywords

Automated teller machine; Convolutional neural network; One time Password; deep learning; Theft prevention

Abstract

ATM machines have become a standard method for financial transactions, but they have also become vulnerable to fraudulent activities. This study presents a method that utilizes a Convolutional Neural Network (CNN) algorithm to detect ATM hammer use as a preventive measure against robberies. A novel ATM security paradigm is proposed, incorporating One-Time Password (OTP) authentication and face recognition to enhance security and consumer privacy. Face recognition eliminates the risk of fraud and duplicate card use, while OTP serves as a dynamic PIN, reducing the need for users to memorize passwords. The system employs TensorFlow for weapon detection, CNN for user identification, and a vibration sensor for detecting unauthorized machine movement. Additionally, the security framework integrates a stepper motor, buzzer, alert notification system, solenoid valve, siren, and door control mechanism. To further enhance security, the system captures and transmits images of individuals carrying weapons inside ATMs to authorized personnel via email, aiding law enforcement in suspect identification. The performance of the proposed system is evaluated using key metrics such as accuracy, specificity, F1-score, and error rate. The results demonstrate a 99.5% accuracy rate, setting a new benchmark for security in the banking sector.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Kamarunisha
AU  - S. Vimalanand
AU  - B. Akthar
AU  - Kiruthika
AU  - S. Dhivyapriya
AU  - T. Aarthi
PY  - 2025
DA  - 2025/06/30
TI  - Convolutional Neural Network Method based Security Solution for Facial Recognition in ATM
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 104
EP  - 120
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_11
DO  - 10.2991/978-94-6463-754-0_11
ID  - Kamarunisha2025
ER  -