Olaniyan O. M.

Work place: Department of Computer Engineering, Federal University, Oye Ekiti, Nigeria

E-mail: olatayo.olaniyan@fuoye.edu.ng


Research Interests:


Olatayo Moses OLANIYAN is an Associate Professor of Computer Engineering at Federal University, Oye Ekiti Nigeria. Having mare than fifteen years’ experience in academics, he has graduated several Masters and PhD students, he is an authority in AI and embedded systems. He has more than 60 publications which include books, journals and proceedings.

Author Articles
Development of a Facenet Enhanced Secured Smart Office System

By Odeyemi C. S. Olaniyan O. M.

DOI: https://doi.org/10.5815/ijisa.2024.01.04, Pub. Date: 8 Feb. 2024

A secured smart office system is the one that is capable of recognizing and granting access to authorized persons only and manage the office appliances autonomously. The goals are access control, security and automation. Over the years, several studies have been carried out to meet these needs using RFID cards, access codes and biometrics resulting in weak security with long computational period. Switching of electrical appliances and smoke detection in case of fire outbreak were used but real time electrical appliances management that could prevent fire outbreak is yet to be achieved. This research focused its attention on the design and implementation of a smart office system that meet these needs. The system was developed using a raspberry pi 4 board. Ultrasonic sensor, camera, servo motor, relay, current and voltage sensors were interfaced with the raspberry pi for image capturing, opening the door, switching and power monitoring respectively. The system captures the image of an approaching person and process it for recognition using FaceNet; an open source model for face recognition. Information was transmitted via SIM800L GSM module as SMS to the administrator. The system shuts down the office electrical network once the supply voltage exceeds 220v ac or less than 161v ac, thus preventing any chance of fire outbreak due to irregular power supply. The accuracy of image recognition model was 93.13%. This research has shown a simple way of implementing an autonomous smart office system that is capable of providing adequate security, efficiency and convenience in offices.

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