Abstract:
This study discusses the design and implementation of an automatic attendance
system using facial recognition technology running on the Raspberry Pi 4B
platform. Manual attendance methods are often considered inefficient, prone to
manipulation, and time-consuming, especially in academic and office
environments. By leveraging biometric technology and low-cost edge computing
devices, this study aims to provide a practical, real-time, and contactless solution
for recording attendance.
The system uses a USB camera connected to the Raspberry Pi to capture facial
images. Face detection is performed using OpenCV with the Haar Cascade
classifier, while face recognition is carried out using the Local Binary Pattern
Histogram (LBPH) algorithm due to its simplicity and efficiency on devices with
limited computational power. Once the face is recognized, the system automatically
records the name and attendance time in a structured .csv file. To enhance usability
and accessibility, the system is also equipped with a lightweight Flask-based web
interface that allows administrators to view, manage, and download attendance data
via a browser.
The entire process—from face detection, recognition, recording, to web display—
is run locally on the Raspberry Pi without requiring a cloud connection,
demonstrating that artificial intelligence technology can be directly implemented
on edge devices. Testing was conducted under various lighting conditions and facial
angles to evaluate the system's accuracy and speed. Results show that the system
can provide reliable recognition rates and efficient processing times for small-scale
applications such as classrooms or offices.
This research contributes to the development of intelligent systems in education and
organizational settings by providing an affordable, scalable, and user-friendly
attendance solution. Further development could include integration with a
centralized database, algorithm improvements using deep learning, and multi-
device synchronization support.
Overall, the implementation of a face recognition-based attendance system on
Raspberry Pi, equipped with a web interface and structured data storage, has proven
to be a practical and efficient tool for attendance automation with minimal human
intervention and high accessibility.
