Attendance System Based on Facial Recognition Using OpenCV

Face recognition is one of the most prevalent applications of image processing, playing a critical role in various industries, particularly in authentication tasks such as school attendance. The “face recognition” attendance system enables high-definition monitoring and advanced computer technologies to identify students based on their facial biostatistics. This system aims to digitize the traditional and labor-intensive method of recording attendance by calling names and maintaining paper records, which are prone to manipulation and proxies. Current biometric and conventional methods also suffer from similar vulnerabilities. This research work proposes a robust solution utilizing the Python OpenCV library to automate the attendance process. The system generates and saves attendance reports in Excel format post face recognition, and it has been tested in diverse conditions, including varying lighting, head movements, and changes in student-camera distance. The proposed approach demonstrates high accuracy and efficiency, significantly reducing manual effort and time commitment. Furthermore, the system is cost-effective and requires minimal installation effort, presenting a reliable and efficient tool for classroom attendance management.