Real Time Attendance Monitoring System Using Face Recognition

A real-time student attendance monitoring system using machine learning techniques is being developed to automate the manual attendance monitoring process in schools. The system will use image processing and computer vision algorithms to detect and recognize student's faces and compare them with a database of registered students. By providing real-time data and automating the monitoring process, the proposed system is expected to significantly reduce the workload for teachers and staff and improve the accuracy and consistency of attendance data. The process involves several steps, including the acquisition of images, improving their quality through pre-processing, identifying important features for recognizing students, and finally using machine learning techniques like MultiTask Cascaded Convolutional Neural Networks (MTCNNs) to accurately classify the students. Additionally, the system will provide comprehensive reporting to help identify patterns and trends in attendance data, which will provide valuable insights for school administrators.