This paper is published in Volume-5, Issue-2, 2019
Area
IoT
Author
Udhaya Kumar N.
Co-authors
Sri Vasu R., Subash S., Sharmila Rani D.
Org/Univ
Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
Pub. Date
11 March, 2019
Paper ID
V5I2-1225
Publisher
Keywords
ATM, Camera, RFID reader, Tag, Relay, Motor, Raspberry Pi 3 deep learning, Open CV, Python, Haar cascades, CCTV, Local binary patterns, Alert message

Citationsacebook

IEEE
Udhaya Kumar N., Sri Vasu R., Subash S., Sharmila Rani D.. ATM- Security using machine learning technique in IoT, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Udhaya Kumar N., Sri Vasu R., Subash S., Sharmila Rani D. (2019). ATM- Security using machine learning technique in IoT. International Journal of Advance Research, Ideas and Innovations in Technology, 5(2) www.IJARIIT.com.

MLA
Udhaya Kumar N., Sri Vasu R., Subash S., Sharmila Rani D.. "ATM- Security using machine learning technique in IoT." International Journal of Advance Research, Ideas and Innovations in Technology 5.2 (2019). www.IJARIIT.com.

Abstract

The idea of designing and implementation of the real-time ATM security project came with the incidents of accessing the ATM by the unauthorized users instead of an authorized user. This project will give access to the user only after identifying the image of the user taken by the CCTV in the ATM and compare the identified image with the image of the user that was stored in the database created during the account creation which comes under the banking session of banks. In some cases the authorized user is not able to use the ATM for some emergency purposes, in such cases, the OTP is sent to the users registered mobile number and the person who came instead of the authorized user have to enter the OTP that the authorized user received. This method will reduce the risk of ATM usage by common people. The face detection and face recognition are done using deep learning techniques and machine learning. The IOT components like Camera, RFID reader, Tag, Relay, Motor were used. The Raspberry pi 3 (2015 version) is used as the main component. Here the open CV is used as the platform and the python language is used for the deep learning techniques and face detection Haar cascade is used for face detection. The face recognition module is done by Local Binary Patterns (LBP) algorithm. And an alert message is sent to the authorized user as a text message if the user is found to be the third party user.
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