Automatic Attendance and Duration Detection System

Abstract

The “Automatic Attendance and Duration Detection System” is an innovative solution designed to streamline attendance tracking processes. This project leverages state-of- the-art machine learning techniques, specifically Convolutional Neural Networks (CNNs), to enable precise and efficient face recognition. And Haar-cascade use for face detection. The system initiates through a user- friendly web interface, which activates the live camera feed for real-time face detection and recognition. Key components of the project include real-time face detection and recognition, enabled by advanced computer vision algorithms, and a CNN-based face recognition model. The system relies on a meticulously assembled dataset for training and a series of critical methods, including data preprocessing, CNN architecture selection, model training, and thorough validation and testing. One distinctive feature of this system is the capability to calculate an individual’s presence duration accurately. If an individual’s appearance exceeds a predefined threshold, such as 60 seconds, the system marks them as “present.” This project represents a significant advancement in attendance tracking, making it more efficient and accurate, with potential applications in various educational and organizational settings.