Facial Recognition-Based Attendance System

Facial recognition stands as one of the most efficient applications in image processing, playing a crucial role in the technical sphere. Identifying human faces is a pressing concern, particularly in verifying student attendance. Utilizing facial biostatistics, an attendance system employing face recognition relies on high-resolution monitoring and advanced computer technologies. The objective of developing this system is to digitize the traditional method of attendance-taking, which involves verbal calls and manual record-keeping. Current attendance procedures are laborious and time-consuming, prone to manipulation through manual recording. Both traditional attendance marking and existing biometric systems are susceptible to fraudulent proxies. This paper aims to address these challenges. The proposed system incorporates the Haar cascade algorithm, OpenCV, Dlib, Pandas, and MySQL. Following facial recognition, attendance reports are generated and saved in Excel format. The system undergoes testing under different conditions, such as variations in illumination, head movements, and changes in camera-to-student distance. Rigorous testing evaluates overall complexity and accuracy. The proposed system proves to be an efficient and robust solution for classroom attendance management, eliminating manual labour and time consumption. Additionally, the system's development is cost-effective and requires minimal installation.