<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>We propose a Computer Vision and Machine Learning equipped model that secures the ATM from fraudulent activities by leveraging the use of Haar cascade (HRC) and Local Binary Pattern Histogram (LBPH) classifier for face detection and recognition correspondingly, which in turn detect fraud by utilizing features, like PIN and face recognition, help to identify and authenticate the user by checking with the trained dataset and trigger real-time alert mail if the user turns out to be unauthorized also. It does not allow them to log in into the machine, which resolves the ATM security issue. this system is evaluated on the dataset of real-world ATM camera feeds, which shows an accuracy of 90%. It can effectively detect many frauds, including identity theft and unauthorized access which makes it even more reliable.</p>
</abstract>

We propose a Computer Vision and Machine Learning equipped model that secures the ATM from fraudulent activities by leveraging the use of Haar cascade (HRC) and Local Binary Pattern Histogram (LBPH) classifier for face detection and recognition correspondingly, which in turn detect fraud by utilizing features, like PIN and face recognition, help to identify and authenticate the user by checking with the trained dataset and trigger real-time alert mail if the user turns out to be unauthorized also. It does not allow them to log in into the machine, which resolves the ATM security issue. this system is evaluated on the dataset of real-world ATM camera feeds, which shows an accuracy of 90%. It can effectively detect many frauds, including identity theft and unauthorized access which makes it even more reliable.

Unlocking the Future of Secure Automatic Machines: Leveraging Facereg with HRC &amp; LBPH

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Unlocking the Future of Secure Automatic Machines: Leveraging Facereg with HRC & LBPH

We propose a Computer Vision and Machine Learning equipped model that secures the ATM from fraudulent activities by leveraging the use of Haar cascade (HRC)...

Unlocking the Future of Secure Automatic Machines: Leveraging Facereg with HRC & LBPH

Publié en ligne: 13 avr. 2024

Pages: 67 - 73

Reçu: 18 juil. 2023

Accepté: 20 oct. 2023

DOI: https://doi.org/10.14313/jamris/1-2024/7

Mots clés
ATM, Computer vision, PIN, HRC, LBPH recognizer, Face detection, Face recognition, Fraud detection, SMTP module

© 2024 Yamini Vijaywargiya et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Unlocking the Future of Secure Automatic Machines: Leveraging Facereg with HRC & LBPH

Publié en ligne: 13 avr. 2024

Pages: 67 - 73

Reçu: 18 juil. 2023

Accepté: 20 oct. 2023

DOI: https://doi.org/10.14313/jamris/1-2024/7

Mots clésATM, Computer vision, PIN, HRC, LBPH recognizer, Face detection, Face recognition, Fraud detection, SMTP module

© 2024 Yamini Vijaywargiya et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
ATM, Computer vision, PIN, HRC, LBPH recognizer, Face detection, Face recognition, Fraud detection, SMTP module