Abstract
We propose a Computer vision and Machine Learning equipped model which secures ATMs from fraudulent activities by leveraging the use of Haar cascade (HRC) and Local Binary Pattern Histogram (LBPH) classifier for face detection and recognition correspondingly, which in turn detect fraud by utilizing features like PIN and also face recognition helps to identify and authenticate the user by checking with the trained dataset and trigger real-time alert mail if the user finds to be unauthorized also, do not allow them to log in into the machine which resolves the ATM security issue. this system is evaluated on the dataset of real-world ATM camera feeds, which shows an accuracy of 90%. It can effectively detect many frauds, including identity theft and unauthorized access which makes it even more reliable.
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