Research In Motion
In this modern world, many of consumers have been largely depending ATMs machine to meet banking needs activities like money transfer, cash withdrawal, paying electricity and phone bill and etc. In Automated Teller Machines (ATMs) fraud became more widespread. Computation of biometric offers an effective approach for ATM customer's identification/verification with unique physical/behavioral characteristics. In this paper, a new framework is designed to enhance level of security at ATMs by doing customer verification with palm print recognition. First, method extracts low frequency palm print features by applying 2D DCT (Discrete Cosine Transform). Features of training and test sets are matched using Euclidian distance.