Cite this DOI

DR. JAGADEESAN, D. J. (2023). FACE MASK DETECTION USING MACHINE LEARNING. *Journal of Science & Technology*, *8*(7), 114–119. https://doi.org/10.46243/jst.2023.v8.i06.pp114-119

@article{drjagadeesan2023face,
  author    = {DR. JAGADEESAN, DR. JAGADEESAN},
  title     = {{FACE MASK DETECTION USING MACHINE LEARNING}},
  journal   = {Journal of Science \& Technology},
  year      = {2023},
  month     = {aug},
  volume    = {8},
  number    = {7},
  pages     = {114--119},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2023.v8.i06.pp114-119},
  url       = {https://doi.org/10.46243/jst.2023.v8.i06.pp114-119},
  language  = {en},
  abstract  = {COVID-19 pandemic has rapidly affected ourday-to-day life disrupting the world trade and movements. Wearing a protective face mask hasbecome a new normal. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. Therefore, face maskdetection has become a crucial task to help global society. This paper presents a simplified approach to achieve this purpose using some basic Machine Learning packages like TensorFlow, Keras and OpenCV. The application of ―machine learning‖ and ―artificial intelligence‖ has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents.The proposed method detects the face from the image correctly and then identifies if it has a mask on it or not. As a surveillance task performer, it can also detect a face along with a mask in motion. The method attains accuracy up to 95.77\% and 94.58\% respectively on two different datasets. We explore optimized values of parameters using the mobileNetV2 which is a Convolutional Neural Network architecture to detect the presence of masks correctly without causing overfitting.}
}

TY  - JOUR
TI  - FACE MASK DETECTION USING MACHINE LEARNING
AU  - DR. JAGADEESAN, DR. JAGADEESAN
JO  - Journal of Science & Technology
PY  - 2023
DA  - 2023/08/07/
VL  - 8
IS  - 7
SP  - 114
EP  - 119
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - COVID-19 pandemic has rapidly affected ourday-to-day life disrupting the world trade and movements. Wearing a protective face mask hasbecome a new normal. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. Therefore, face maskdetection has become a crucial task to help global society. This paper presents a simplified approach to achieve this purpose using some basic Machine Learning packages like TensorFlow, Keras and OpenCV. The application of ―machine learning‖ and ―artificial intelligence‖ has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents.The proposed method detects the face from the image correctly and then identifies if it has a mask on it or not. As a surveillance task performer, it can also detect a face along with a mask in motion. The method attains accuracy up to 95.77% and 94.58% respectively on two different datasets. We explore optimized values of parameters using the mobileNetV2 which is a Convolutional Neural Network architecture to detect the presence of masks correctly without causing overfitting.
DO  - 10.46243/jst.2023.v8.i06.pp114-119
UR  - https://doi.org/10.46243/jst.2023.v8.i06.pp114-119
ER  -

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    "URL": "https://doi.org/10.46243/jst.2023.v8.i06.pp114-119",
    "title": "FACE MASK DETECTION USING MACHINE LEARNING",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
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            "family": "DR. JAGADEESAN",
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    "language": "en",
    "abstract": "COVID-19 pandemic has rapidly affected ourday-to-day life disrupting the world trade and movements. Wearing a protective face mask hasbecome a new normal. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. Therefore, face maskdetection has become a crucial task to help global society. This paper presents a simplified approach to achieve this purpose using some basic Machine Learning packages like TensorFlow, Keras and OpenCV. The application of ―machine learning‖ and ―artificial intelligence‖ has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents.The proposed method detects the face from the image correctly and then identifies if it has a mask on it or not. As a surveillance task performer, it can also detect a face along with a mask in motion. The method attains accuracy up to 95.77% and 94.58% respectively on two different datasets. We explore optimized values of parameters using the mobileNetV2 which is a Convolutional Neural Network architecture to detect the presence of masks correctly without causing overfitting.",
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2023.v8.i06.pp114-119 gives all four in one JSON answer.