A Comprehensive Examination of Biometric ATM Operations Involving Fingerprint and Face Recognition Using Deep Learning

India's technological advancements have significantly enhanced convenience and reliability, especially in the banking sector, benefiting consumers with notable progress. Automated Teller Machines (ATMs) have revolutionized transaction processes, reducing the chances of errors by human intervention. ATMs facilitate cash withdrawals and deposits 24/7, with seamless integration via bank-issued cards. Despite these advantages, there has been a rise in incidents of card theft and fraudulent transactions, posing challenges to the security and trustworthiness of ATMs. To bolster security measures, a shift from card-based to person-based identification during transactions is imperative. The adoption of a biometric authentication system is crucial for ensuring user verification through a virtual ATM approach. This method involves utilizing face and fingerprint recognition technologies with live streaming, Channel Boosted Convolutional Neural Networks, and One Time Password implementation to establish a highly secure and dependable virtual ATM system. Further research will delve into the details of this proposed strategy.