Date of Publication :15th June 2024

Abstract:The project focuses on the urgent need for automated systems to enforce mask-wearing protocols in public settings amidst the COVID-19 pandemic. Leveraging convolutional neural networks (CNNs) within TensorFlow and Keras libraries in Python, the development centers on constructing a robust face mask detection classifier. A diverse dataset sourced from the internet, featuring images of individuals with and without masks, serves as the foundation for training and testing. The CNN architecture is meticulously designed to extract relevant features from input images, enabling accurate predictions regarding mask presence. Key to assessing the model's effectiveness is its accuracy, quantifying the percentage of correctly classified images within the test dataset. Achieving high accuracy is paramount for real-world deployment, where the classifier can automate mask detection in surveillance systems or mobile applications, bolstering efforts to curb the spread of infectious diseases. Ethical considerations, including privacy and bias concerns, are carefully addressed throughout the project's development and deployment phases, underscoring the importance of transparency, fairness, and accountability in technological solutions for public health challenges.

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