Nonlinear SVM Classifier based Online Attendance System for Facial Recognition and Mask Detection

This research presents a novel method for monitoring attendance by creating an online system that combines facial recognition technology with machine learning algorithms to detect masks. The main objective of this project is to provide a streamlined attendance management system that can be accessed through web browsers, removing the requirement for the installation of specialised software. The technology utilises facial biometrics to identify users, storing profiles with facial picture samples in a centralised online database. Before implementing facial recognition, the system goes through a model training phase where Support Vector Machines (SVM) are used to create a strong recognition model. Moreover, synthetic data is utilised to train the algorithm in recognising individuals who are wearing facial masks. The server-side application is developed using Python and utilises the OpenCV library for image processing. The web interfaces are managed through PHP, while the database is handled using MySQL. The combination of Python and PHP scripting enables immediate processing on web servers, guaranteeing availability from any device through a web browser. The experimental findings indicate that the face recognition system achieved an accuracy rate of around $81.8 \%$, while the mask detection system achieved an accuracy rate of $80 \%$. These results were obtained using a pre-trained model. In summary, this system provides a whole solution for effective and safe online attendance management in many fields.