Dual-Factor Indoor Identity Verification System Using Face Recognition and Height Detection with Raspberry Pi
| International Journal of Electrical and Electronics Engineering |
| © 2025 by SSRG - IJEEE Journal |
| Volume 12 Issue 4 |
| Year of Publication : 2025 |
| Authors : Keeradit Saiapattalung, Sarawut Puttaraksa, Ittikorn Punnoy |
10.14445/23488379/IJEEE-V12I4P108
How to Cite?
Keeradit Saiapattalung, Sarawut Puttaraksa, Ittikorn Punnoy, "Dual-Factor Indoor Identity Verification System Using Face Recognition and Height Detection with Raspberry Pi," SSRG International Journal of Electrical and Electronics Engineering, vol. 12, no. 4, pp. 121-131, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I4P108
Abstract:
The purpose of this system was to verify identities and positions indoors by using image processing technology that detects a person’s facial characteristics and height to increase identity verification accuracy. Data on people registered to use the system were stored in the Firebase database connected to Raspberry Pi boards, where cameras were installed at various positions of the test area for real-time monitoring and detection. Six cameras were installed, with three cameras being installed on two levels with an area of 746 square meters per level. Cameras were installed 0.40 meters from the ceiling or 2.1 meters from the floor. The system’s main components consisted of Raspberry Pi4 boards, pocket WiFi, webcams, the Firebase database, and API linking data collection, detection, and warnings. Height was tested and determined by determining width compared to the reference size, and face testing was done using a 67-position model via processing with the MediaPipe library. System test results had a likeness of face verification from six cameras at a mean of 58.15-61.28%, while the mean height from measurements had a mean error of 0.14%-1.53%, which did not exceed 2% and was acceptable. In addition, the system was able to detect faces at a range of 0.5-4 meters when installed at heights of 1-2.5 meters, with the time of 8:30 am - 4:30 pm being the optimal time for facial recognition in the test area. Two-level identity verification was shown to be able to identify individuals effectively.
Keywords:
Real-time identity verification, Image processing, Face recognition, Height detection, Indoor tracking system.
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