Principal Component Analysis for ATM Facial Recognition Security

  • R. Regin Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, India
  • S. Suman Rajest Professor, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India
  • Shynu T Master of Engineering, Department of Biomedical Engineering, Agni College of Technology, Chennai, Tamil Nadu, India
  • Steffi. R Assistant Professor, Department of Electronics and Communication, Vins Christian College of Engineering, Tamil Nadu, India
Keywords: Human Face Recognition, Automated Teller Machine, Principal Component Analysis, Eigenvalue, Eigen's face


The Automated Teller Machine, also known as an ATM, has become the most common method by which individuals withdraw cash for their own use. The transactions that people conduct through the ATM ought to be protected, and the particulars ought to be kept private. Facial recognition will be used as the method of user authentication in this project so that Automated Teller Machine (ATM) transactions will be protected from fraudulent activities. The image of the account holder's face, as well as the faces of any beneficiaries, must be taken in well-lit conditions and then saved on a central server for face recognition to work. A camera is positioned within the automated teller machine (ATM) in such a way that it can take a picture of the person who is currently using it. After comparing the face of the cardholder to the account holder and beneficiaries, the system moves on to the PIN Validation step after recognising the cardholder's identity


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How to Cite
Regin, R., Rajest, S. S., T, S., & R, S. (2023). Principal Component Analysis for ATM Facial Recognition Security. Central Asian Journal of Medical and Natural Science, 4(3), 292-311.