Abstract: The Face Mask Identification With Automated Door Entry Control using Deep Learning addresses the critical need for public health safety during the COVID-19 pandemic by automating the process of identifying individuals wearing face masks in public spaces. This system utilizes deep learning techniques to detect faces and classify whether the individuals are wearing masks or not, thereby allowing automated door entry based on compliance with face mask regulations. The system has undergone extensive testing and validation, proving its efficacy in correctly recognizing people who are wearing face masks and giving access in accordance with that identification. Results indicate high accuracy, speed, and reliability, with potential implications for improving public health and safety measures in various settings. Moreover, when the system detects an individual not wearing a mask, it provides a warning message through voice output, urging them to wear their mask. Conversely, when the system identifies a person wearing a mask, it delivers a message of acknowledgment, thanking them for their compliance. overall our project offering insights into its development, implementation, and potential impact on public health and safety.

Keywords: covid-19,deep learning, Mask Identification ,voice, door entry

Cite:
G.Venkateswari, B.Venkata Lakshmi, A.padma, K.Sandhya Rani, "Face Mask Identification With Automated Door Entry Control Using Deep Learning", IARJSET International Advanced Research Journal in Science, Engineering and Technology, vol. 11, no. 3, 2024, Crossref https://doi.org/10.17148/IARJSET.2024.11320.

| DOI: 10.17148/IARJSET.2024.11320