Face based Attendance and Behaviour Tracking

A high-accuracy system for multi-face detection, recognition, one of the busiest uses of image processing is face recognition, which is essential to the industry. The ability to recognize a human face is a current concern for authentication, particularly when it comes to student attendance. A highaccuracy system for multi-face detection, recognition, and behaviour tracking is challenging task in the modern industry sector. The technology uses sophisticated face identification algorithms and recognition models by utilizing datasets and deep learning. The GUI application for the same improves user interaction, ensuring strong performance for real-world applications. This project’s goal is to improve attendance accuracy, simultaneously recognizing several faces at once using MTCNN (Multi-task Cascaded Convolutional Neural Networks) algorithm and documenting the data. This study investigates how deep learning approaches can help with less time-consuming attendance tracking.