Facial Recognition & Eye Blink Secured Cardless ATM using CNN

Facial Recognition & Eye Blink Secured Cardless ATM using CNN

Shaikh Fahad Naushad, Shubham Krushna Sirsat,Tushar Chandrabhan Dange, Kiran Sanjay Hyalij, Prof. (Dr)Amol Potgantwar

Department of Computer Engineering
Sandip Institute of Technology and Research Centre Nashik,

Abstract

Automated Teller Machine (ATM) transactions have become an essential and secure method for fulfilling our financial commitments. Traditionally, using an ATM requires a debit card, but there are times when account balances run low, or individuals forget to carry their cards, leading to transaction difficulties. Simultaneously, the use of mobile phones has become ubiquitous. Connecting these electronic devices has sparked a simple and efficient approach to withdrawing cash without the need for a debit card. This innovative method can be described as “Cardless Cash Withdrawal” and is fortified with two layers of security; Facial Recognition and Eye Blink Authentication, both powered by Convolutional Neural Networks (CNN). The system employs state-of-the-art CNN models to verify the user’s identity based on their unique facial features. This biometric authentication adds an extra layer of security, ensuring that only the authorized user can access the ATM. Besides facial recognition, the system utilizes eye blink patterns as a secondary authentication method. Analyzing the user’s Eye blinks, further validates their identity. This sophisticated technology enhances security and reduces the risk of unauthorized access. A one-time Password (OTP) is used to complete the authentication process, the system generates a one-time password that is sent to the user’s mobile phone. This OTP serves as an additional verification step, ensuring that the individual initiating the transaction is the legitimate account holder.