Student Facial Authentication Model based on OpenCV’s Object Detection Method and QR Code for Zambian Higher Institutions of Learning

Facial biometrics captures human facial physiological data, converts it into a data item variable so that this stored variable may be used to provide information security services, such as authentication, integrity management or identification that grants privileged access or control to the owner of that data variable. In this paper, we propose a model for student authentication based on facial biometrics. We recommend a secure model that can be used in the authentication and management of student information in the registration and access of resources, such as bursaries, student accommodation and library facilities at the University of Zambia. Since the model is based on biometrics, a baseline study was carried out to collect data from the general public, government entities, commercial banks, students, ICT regulators and schools on their understanding, use and acceptance of biometrics as an authentication tool. Factor analysis has been used to analyze the findings. The study establishes that performance expectancy, effort expectancy, social influence and user privacy are key determinants for application of a biometric multimode authentication. The study further demonstrates that education and work experience are regulating factors on acceptance and expectancy of a biometric authentication system. Based on these results, we then developed a biometric model that can be used to perform authentication for students in higher learning institutions in Zambia. The results of our proposed model show 66% acceptance rate using OpenCV.

@article{Musambo2018,
  title     = {Student Facial Authentication Model based on OpenCV’s Object Detection Method and QR Code for Zambian Higher Institutions of Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {5},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Lubasi Kakwete Musambo and Jackson Phiri},
  doi       = {10.14569/IJACSA.2018.090512},
  url       = {https://doi.org/10.14569/IJACSA.2018.090512}
}