Automatic Phizog Based Attendance Monitoring System

Abstract

Facial recognition technology has emerged as a robust solution for various applications, including attendance management systems. Traditional methods, such as paper-based or RFID systems, often suffer from inaccuracies and are cumbersome to manage. To address these limitations, this smart facial recognition system (SFRAS) is designed with a primary objective of creating an efficient and user-friendly attendance management system. The system was implemented of the system involves integrating a Raspberry Pi single-chip computer with a camera module to capture real-time images of individuals upon their arrival in a designated area which are processed for extracting features using Open CV’s facial recognition algorithms. The extracted features are then compared against a data base of pre-registered faces to determine the identity of individuals. Upon successful identification, the attendance record is updated in a centralized database. Python scripts are employed to orchestrate the interaction between the Raspberry Pi, OpenCV, and the database. This SFRAS with Raspberry Pi as a hardware platform ensures cost-effectiveness and scalability, making it suitable for deployment for automating attendance management processes in various environments such as schools, offices, and organizations of all sizes. provides real-time monitoring and reporting capabilities, enabling administrators to efficiently manage attendance data. In conclusion, the Facial Attendance System proposed demonstrates the potential of combining Raspberry Pi, OpenCV, and Python to create an innovative and efficient solution for attendance management. With further refinement and optimization, this system has the potential to revolutionize how organizations track and manage attendance in the digital age.