FACEPIN: FACE BIOMETRIC AUTHENTICATION SYSTEM FOR ATM USING DEEP LEARNING

Abstract

Automated Teller Machines also known as ATM
are widely used nowadays by each and everyone. There is an urgent need for improving security in banking region. Due to
tremendous increase in the number of criminals and their activities, the ATM has become insecure. ATM systems today use
no more than an access card and PIN for identity verification. The recent progress in biometric identification techniques,
including finger printing, retina scanning, and facial recognition has made a great effort to rescue the unsafe situation at the
ATM. This project proposes an automatic teller machine security model that would combine a physical access card and
electronic facial recognition using Deep Convolutional Neural Network. If this technology becomes widely used, faces would
be protected as well as their accounts. Face Verification Link will be generated and sent to user to verify the identity of
unauthorized user through some dedicated artificial intelligent agents, for remote certification. However, it obvious that
man’s biometric features cannot be replicated, this proposal will go a long way to solve the problem of Account safety making
it possible for the actual account owner alone have access to his accounts.