Development of an Attendance Management System Using Facial Recognition Technology

Aims: The rise of automation has greatly impacted various aspects of daily life, including attendance management systems. Traditional methods like manual roll calls and card swiping are often inefficient and prone to errors, leading researchers to explore innovative alternatives such as face recognition technology. This technology employs advanced algorithms and image processing techniques to accurately identify individuals based on their unique facial features, thereby enhancing accuracy and security measures. The objective of this project is to develop a face recognition-based attendance system with specific goals, including circuit design, simulation using Proteus Software, implementation, and performance evaluation through rigorous field-testing.

Study Design: The system comprises an input subsystem with a Camera module and two output subsystems: an LCD and a Web-database PC software. Initially, custom and stored image data of students are saved in a microSD card of the camera module, and attendance records are updated on a web-database upon successful scanning and recognition of students' faces.

Methodology: The implementation process involves assembling various hardware components, including securely mounting the ESP32 CAM and integrating a 16x4 Character LCD Display for visual feedback. LEDs are used as visual indicators for system statuses, while a Power Bank Module ensures consistent power supply. The system is controlled by the ESP32-CAM, which captures and verifies faces, displays actions on the LCD, and updates the web-database with attendance data.

Results: Testing results indicate the system's high accuracy, achieving a 100% attendance rate across multiple class sessions with zero misclassifications.

Conclusion: The user-friendly interface and seamless wireless connectivity enhance accessibility and real-time monitoring, making it a valuable tool for classroom management and office use. The research has some hardware limitations which includes processing large amounts of facial data requires robust hardware. Inadequate camera resolution or processing power can lead to slow performance or inaccurate recognition. Overall, the face recognition-based attendance system has the potential to revolutionize traditional methods, offering high accuracy, user-friendly features, and robust connectivity, with possible applications beyond education through further refinement and research. Facial recognition-based attendance systems are more accurate and efficient than traditional methods like manual roll calls or card swiping. By automating attendance, they reduce errors and provide real-time updates with web integration. Despite hardware limitations, the system's accuracy and ease of use make it a superior alternative with potential for broader applications.