Abstract
In real-time face mask detection system developed using Python, TensorFlow, and Keras, designed to enhance public health safety during the COVID-19 pandemic. Leveraging computer vision techniques, the system detects faces in video streams and accurately classifies the presence and correct positioning of face masks. Green and red rectangular signs overlaying individuals' faces provide real-time feedback on mask compliance. The system's versatility enables seamless integration into various surveillance infrastructures, making it a practical solution for enforcing mask-wearing mandates in high-traffic public spaces. Its implementation using widely accessible libraries enhances its potential for widespread adoption, highlighting the role of technology in mitigating the spread of infectious diseases. Key Words: face mask detection, real-time, Python, TensorFlow, Keras, computer vision, machine learning, public health safety, COVID-19, surveillance.
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