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    Proposal For FMD

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    Mohamed Ahmed
    The document discusses the development of a face mask detection system to help curb the spread of COVID-19. It describes creating an ensemble model combining one-stage and two-stage object detectors using transfer learning to accurately detect masked and unmasked faces in real-time video streams. The system was trained on a dataset of over 3,800 images and achieves over 98% accuracy. It is intended to be deployed in public areas using cameras to identify those not wearing masks and enforce mask requirements.

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    Proposal For FMD

    Uploaded by

    Mohamed Ahmed
    26 views8 pages
    The document discusses the development of a face mask detection system to help curb the spread of COVID-19. It describes creating an ensemble model combining one-stage and two-stage object detectors using transfer learning to accurately detect masked and unmasked faces in real-time video streams. The system was trained on a dataset of over 3,800 images and achieves over 98% accuracy. It is intended to be deployed in public areas using cameras to identify those not wearing masks and enforce mask requirements.

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    Proposal for FMD

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    The document discusses the development of a face mask detection system to help curb the spread of COVID-19. It describes creating an ensemble model combining one-stage and two-stage object detectors using transfer learning to accurately detect masked and unmasked faces in real-time video streams. The system was trained on a dataset of over 3,800 images and achieves over 98% accuracy. It is intended to be deployed in public areas using cameras to identify those not wearing masks and enforce mask requirements.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    26 views8 pages

    Proposal For FMD

    Uploaded by

    Mohamed Ahmed
    The document discusses the development of a face mask detection system to help curb the spread of COVID-19. It describes creating an ensemble model combining one-stage and two-stage object detectors using transfer learning to accurately detect masked and unmasked faces in real-time video streams. The system was trained on a dataset of over 3,800 images and achieves over 98% accuracy. It is intended to be deployed in public areas using cameras to identify those not wearing masks and enforce mask requirements.

    Copyright:

    © All Rights Reserved
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    of 8

    Face Mask Detection

    Supervised by

    Prof.Dr./ Ayman El-Sayed

    Project members

    1. Moamen mamdouh elgamal


    2. Moamen said khalafallah
    3. Mahmoud Hassan elbarbary
    4. Mohamed ahmed abo donia
    5. Mohamed elnabawy soliman
    6. Youmna ali khlaifa
    7. Mayada ahmed ewida
    8. Donia Mohamed Ibrahim
    Effective strategies to restrain COVID-19 pandemic
    need high attention to mitigate negatively impacted
    communal health and global economy, with the
    brim-full horizon yet to unfold. In the absence of
    effective antiviral and limited medical
    resources, many measures are recommended by
    who to control the infection rate and avoid
    exhausting the limited medical resources.
    Wearing a mask is among the non-pharmaceutical
    intervention measures that can be used to cut the
    primary source of SARS-CoV2 droplets expelled
    by an infected individual. Regardless of discourse
    on medical resources and diversities in masks, all
    countries are mandating coverings over the nose
    and mouth in public. To contribute towards
    communal health, this paper aims to devise
    a highly accurate and real-time technique
    that can efficiently detect non-mask faces
    in public and thus, enforcing to wear mask.
    The proposed technique is ensemble of
    one-stage and two-stage detectors to achieve
    low inference time and high accuracy.

    The 209th report of the world health organization


    (WHO) published on 16th August 2020 reported that
    coronavirus disease (COVID-19) caused by acute
    respiratory syndrome (SARS-CoV2) has globally
    infected more than 6 Million people and caused over
    379,941 deaths worldwide. According to Carissa F.
    Etienne, Director, Pan American Health Organization
    (PAHO), the key to control COVID-19 pandemic is
    to maintain social distancing, improving surveillance
    and strengthening health systems.
    |Page1
    Recently, a study on understanding measures to
    tackle COVID-19 pandemic carried by the
    researchers at the University of Edinburgh reveals
    that wearing a face mask or other covering over the
    nose and mouth cuts the risk of Coronavirus spread
    by avoiding forward distance travelled by a person’s
    exhaled breath by more than 90% . Steffen et al. also
    carried an exhaustive study to compute the
    community-wide impact of mask use in general
    public, a portion of which may be asymptomatically
    infectious in New York and Washington. The
    findings reveal that near universal adoption (80%) of
    even weak masks (20% effective) could prevent 17–
    45% of projected deaths over two months in New
    Work and reduces the peak daily death rate by 34–
    58%. Their results strongly recommend the use of
    the face masks in general public to curtail the spread
    of Coronavirus. Further, with the reopening of
    countries from COVID-19 lockdown, Government
    and Public health agencies are recommending face
    mask as essential measures to keep us safe when
    venturing into public. To mandate the use of
    facemask, it becomes essential to devise some
    technique that enforce individuals to apply a mask
    before exposure to public places . Face mask
    detection refers to detect whether a person is wearing
    a mask or not. In fact, the problem is reverse
    engineering of face detection where the face is
    detected using different machine learning algorithms
    for the purpose of security, authentication and
    surveillance. Face detection is a key area in the field
    of Computer Vision and Pattern Recognition .

    |Page2
    Face mask detection refers to detect whether a
    person is wearing a mask or not. In fact, the
    problem is reverse engineering of face detection
    where the face is detected using different machine
    learning algorithms for the purpose of security,
    authentication and surveillance. Face detection is a
    key area in the field of Computer Vision and
    Pattern Recognition. A significant body of
    research has contributed sophisticated to
    algorithms for face detection in past. The primary
    research on face detection was done in 2001 using
    the design of handcraft feature and application of
    traditional machine learning algorithms to train
    effective classifiers for detection and recognition .
    The problems encountered with this approach
    include high complexity in feature design and low
    detection accuracy. In recent years, face detection
    methods based on deep convolutional neural
    networks (CNN) have been widely developed to
    improve detection performance.
    Although numerous researchers have committed
    efforts in designing efficient algorithms for face
    detection and recognition but there exists an
    essential difference between ‘detection of the face
    under mask’ and ‘detection of mask over face.’
    As per available literature, very little body of
    research is attempted to detect mask over face.
    Thus, our work aims to a develop technique that
    can accurately detect mask over the face in public
    areas (such as airports).

    |Page3
    railway stations, crowded markets, bus stops, etc.) to
    curtail the spread of Coronavirus and thereby
    contributing to public healthcare. Further, it is not
    easy to detect faces with/without a mask in public as
    the dataset available for detecting masks on human
    faces is relatively small leading to the hard training of
    the model. So, the concept of transfer learning is used
    here to transfer the learned kernels from networks
    trained for a similar face detection task on an
    extensive dataset. The dataset covers various face
    images including faces with masks, faces without
    masks, faces with and without masks in one image
    and confusing images without masks. With an
    extensive dataset containing 45,000 images, our
    technique achieves outstanding accuracy of 98.2%.
    The major contribution of the proposed work is given
    below:
    1_Develop a novel object detection method that
    combines one-stage and two-stage detectors for
    accurately detecting the object in real-time from
    video streams with transfer learning at the back end.
    2_Improved affine transformation is developed to
    crop the facial areas from uncontrolled real-time
    images having differences in face size, orientation
    and background. This step helps in better localizing
    the person who is violating the facemask norms in
    public areas/ offices.
    3_ Creation of unbiased facemask dataset with
    imbalance ratio equals to nearly one.
    4_ The proposed model requires less memory,
    making it easily deployable for embedded devices
    used for surveillance purposes.

    |Page4
    more than fifty countries around the world have recently
    initiated wearing face masks compulsory.
    People have to cover their faces in public, supermarkets,
    public transports, offices, and stores. Retail
    companies often use software to count the number of people
    entering their stores. They may also like
    to measure impressions on digital displays and promotional
    screens. We are planning to improve our
    Face Mask Detection tool and release it as an open-source
    project. Our software can be equated to
    any existing USB, IP cameras, and CCTV cameras to detect
    people without a mask. This detection
    live video feed can be implemented in web and desktop
    applications so that the operator can see
    notice messages. Software operators can also get an image in
    case someone is not wearing a mask.
    Furthermore, an alarm system can also be implemented to
    sound a beep when someone without a
    mask enters the area. This software can also be connected to
    the entrance gates and only people wearing face masks can
    come in.

    To mitigate the spread of COVID-19 pandemic, measures


    must be taken. We have modeled a face mask detector
    learning methods in neural networks. To train, validate and
    test the model, we used the dataset that consisted of 1916
    masked faces images and 1919 unmasked faces images. These
    images were taken from various resources like Kaggle and
    RMFD datasets. The model was inferred on images and live
    video streams. To select a base model, we evaluated the
    metrics like accuracy, precision and recall and selected
    MobileNetV2 architecture with the best performance having
    100% precision and 99% recall. It is also computationally
    efficient using MobileNetV2 which makes it easier to install
    the model to embedded systems. This face mask detector can
    be deployed in many areas like shopping malls, airports and
    other heavy traffic places to monitor the public and to avoid
    the spread of the disease by checking who is following basic
    rules and who is not.
    |Page5
    We implemented our model on images containing one and more faces. We
    also implemented it on videos and live video streams by removing and
    wearing masks one by one. Some screenshots of the results are shown
    below:

    One person with mask and one person without mask.

    Both persons with masks.

    Both persons without mask

    |Page6
    1. Open CV
    TechStack/framework 2. Caffe-based face detector
    Used:- 3. Keras
    4. TensorFlow
    5. MobileNetV2

    [1] B. QIN, D. LI, identifying facemask-wearing condition using


    image super-resolution prevent covid-19 (2020).
    [2] S. Li, X. Ning, L. Yu, L. Zhang, X. Dong, Y. Shi, W. He, 2020
    International Conference on High Performance Big Data and
    Intelligent Systems (HPBD&IS) (IEEE, 2020), pp. 1–5
    [3] P. Khandelwal, A. Khandelwal, S. Agarwal, using computer
    vision to enhance safety of workforce in manufacturing in a post
    covid world, arXiv preprint arXiv:2005.05287 (2020).
    [4] M. Jiang, X. Fan, Retinamask: A face mask detector, arXiv
    preprint arXiv:2005.03950 (2020)
    [5] 5. N.C. Ristea, R.T. Ionescu, are you wearing a mask? improving
    mask detection from speech using augmentation by cycle-consistent
    gans, arXiv preprint arXiv:2006.10147 (2020).
    [6] A. Haldorai and A. Ramu, Security and channel noise
    management in cognitive radio networks, Computers & Electrical
    Engineering, vol. 87, p. 106784, Oct. 2020.
    doi:10.1016/j.compeleceng.2020.106784
    [7] A. Haldorai and A. Ramu, Canonical Correlation Analysis Based
    Hyper Basis Feedforward Neural Network Classification for Urban
    Sustainability, Neural Processing Letters, Aug. 2020.
    doi:10.1007/s11063-020-10327-3.
    [8] Fuchs, E. J. (n.d.). Face mask removal time of four face mask
    extrication devices.
    |Page7

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