Abstract
ATMs, also known as Automated Teller Machines, are commonly used nowadays. Despite its advantages, it compels the need to safeguard the financial industry, particularly ATMs, as the crime rate associated with ATM theft increases. For identification and verification, ATM systems typically utilize the access card and PIN. ATM security is being improved by innovative biometric authentication technology such as retina scanning and face recognition. Here, the Deep Convolutional Neural Network (DCNN) is used to construct an encryption model for automated teller machines (ATMs) by combining the physical access cards with electronic facial recognition method. A unique face identification link would be created to validate unauthorized user by employing a dedicated artificial intelligence model to perform remote monitoring.
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