Abstract:
The goal of the "Automated Employee Monitoring System" is to modernize the current
manual system by utilizing computerized equipment and software. This web application can
store and access employee data efficiently and prevent redundant entries. The project aims
to use facial recognition algorithms to identify and track employees, determining if they are
present and working at their desks. The report examines various techniques for facial
recognition, including image processing steps such as preprocessing, segmentation, and
feature extraction. The software is developed using artificial neural networks, which are
particularly useful for feature extraction and detection in character recognition. The system
begins by preprocessing the captured image, followed by filtering, segmentation, resizing,
and feature extraction. The feedforward process through the network then yields an output
matrix, allowing for the identification of the recognized character. The programming
language used for the end product of the algorithms is Python.
