COVID-19 CONTACT TRACING USING ACCESS CONTROL AND FACEMASK RECOGNITION

Abstract

Since the outbreak of the Covid-19 pandemic, Facial Recognition technologies have experienced rapid and extensive adoption worldwide. Initially designed for regulating access to specific facilities and ensuring compliance with mask-wearing protocols, these systems now require enhancements to extend their utility beyond the pandemic. This study aims to augment an existing facemask detection system by incorporating future-proof functionalities, particularly Facial Recognition. The inclusion of access control features, such as Facial Recognition, seeks to advance the system's capabilities, allowing for improved user identification and access management. A TensorFlow Lite machine learning facemask detection model was developed, utilizing a dataset collected from GitHub and Kaggle. The dataset consisted of 5,092 photos categorized into three groups: "with_mask," "without_mask," and "mask_worn_incorrect." To ensure accurate model performance, 70% of these images were allocated to the training set, while the remaining 30% were assigned to the test set. Subsequently, a Python application was created to incorporate this robust facemask recognition model. Notably, the Python application goes beyond mere facemask detection by incorporating Facial Recognition capabilities. The Facial Recognition functionality was implemented using the haarcascade_frontalface_default algorithm. Deployed on a Raspberry Pi 4 edge device, the Python application streamlines user registration by assigning each participant a unique ID based on their National Registration Number (N.R.C). The integration of Facial Recognition technology strengthens the system's ability to accurately identify individuals, reducing the chances of impersonation or unauthorized access, while also enforcing facemask regulations. The proposed solution contributes to the ongoing efforts to create safer and more efficient access control systems in a post-pandemic world.