Face Recognition System for Automatic Door Access Control

https://doi.org/10.47191/etj/v8i2.03
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Authors

  • Oghogho Ikponmwosa Electrical/Electronic Engineering Department, Delta State University, Oleh Campus, Abraka, State, Delta State. Nigeria.
  • Otuagoma S. Electrical/Electronic Engineering Department, Delta State University, Oleh Campus, Abraka, State, Delta State. Nigeria.
  • Ufuoma Jeffrey Okieke Electrical/Electronic Engineering Department, Delta State University, Oleh Campus, Abraka, State, Delta State. Nigeria.
  • Ebimene E. E. Electrical/Electronic Engineering Department, Delta State University, Oleh Campus, Abraka, State, Delta State. Nigeria.
  • Dania B. P. Electrical/Electronic Engineering Department, Delta State University, Oleh Campus, Abraka, State, Delta State. Nigeria.
  • Azubogu D. I. Electrical/Electronic Engineering Department, Delta State University, Oleh Campus, Abraka, State, Delta State. Nigeria.
  • Anamonye U.G. Electrical/Electronic Engineering Department, Delta State University, Oleh Campus, Abraka, State, Delta State. Nigeria.
  • Oyubu A. O. Electrical/Electronic Engineering Department, Delta State University, Oleh Campus, Abraka, State, Delta State. Nigeria.
  • Okpare A.O. Electrical/Electronic Engineering Department, Delta State University, Oleh Campus, Abraka, State, Delta State. Nigeria.
  • Eyenubo O.J. Electrical/Electronic Engineering Department, Delta State University, Oleh Campus, Abraka, State, Delta State. Nigeria.
  • Efenedo G. I. Electrical/Electronic Engineering Department, Delta State University, Oleh Campus, Abraka, State, Delta State. Nigeria.
  • Okpeki K. U Electrical/Electronic Engineering Department, Delta State University, Oleh Campus, Abraka, State, Delta State. Nigeria.
Vol. 8 No. 2 (2023): VOLUME 08 ISSUE 02
Articles
February 16, 2023

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A face recognition system for automatic door access control has been developed in this work with a view to providing a relatively more robust and foolproof access control which can provide better security and reduce human errors inherent in other conventional methods.

The system was designed with machine learning and artificial intelligence to capture faces, train faces with machine mode, and run trained faces to grant access to the user. The system uses the RaspberryPi module, camera module, servo motor and the GSM module which were all incorporated into the fabricated building to make up the prototype developed to provide access control by means of facial biometrics. In order to grant access to registered users, various photos of the users were taken in different positions and expressions with proper illumination. The user’s face is been captured by the camera module and saved in the database with the help of Raspberry Pi Module. Good lighting condition and other favorable conditions helps the camera module to recognize faces and sends signal to the Raspberry Pi which processes these images and opens the door with the help of the servo motor.

The developed prototype was used to train fifty (50) users. It granted access to all fifty (50) users when there was proper illumination and pose but five (5) and nine (9) users respectively were denied access due to challenges of poor illumination and pose variation.