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Mask Wearing Detection Using OpenCV Training Data

Mask Wearing Detection Using OpenCV Training Data


Poster presentation at The 50th Fall Comprehensive Conference of the Korea Information and Communication Society (KIICE) (제50회 한국정보통신학회 추계종합학술대회).

Using haarcascade XML files that can automatically detect faces, eyes, mouths, and noses, we perform a simple mask wearing detection using a common PC webcam.


Aaron Snowberger

October 28, 2021

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  1. OpenCV 학습 데이터를 이용한 마스크 착용 감지 Mask Wearing Detection

    Using OpenCV Training Data Aaron Daniel Snowberger, Choong Ho Lee Mask No Mask
  2. INTRODUCTION It is an important issue to detect automatically whether

    a mask is worn or not for corona prevention. It is known that mask wearing detection can be solved by learning the face data set. However, the search for whether a person is wearing a mask can be detected in a simpler way using OpenCV. In this paper, we describe that it is possible to easily detect whether a single person is wearing a mask or not with a general PC camera using OpenCV learning data results and simple OpenCV functions. Through experiments, the proposed method was shown to be effective.
  3. BUILT WITH PYTHON 3.9.7 OPENCV 4.5.1 TENSORFLOW 2.5.1 haarcascade_frontalface_default.xml haarcascade_eye.xml

    haarcascade_mcs_mouth.xml haarcascade_mcs_nose.xml
  4. FIRST, FIND THE FACE AREA In the upper half of

    the face area EYES In the lower half of the face area MOUTH In the vertical ⅓ ~ ⅔ of the face area NOSE

    and mouth are found, the mask is not worn If nose and mouth are not found, it is judged that the mask is worn No Mask Mask
  6. EXTENDED EXPERIMENTATION Camera angle & lighting matters. Some mistakes in

    suboptimal conditions. 01Angle / light 02 It’s possible to detect partially worn masks if nose or mouth is detected. 03 It’s possible to trick the camera by obscuring the nose & mouth. COVER it up Chin diaper
  7. CONCLUSION Instead of re-learning with a mask-wearing face, we implemented

    a method of retrieving a mask when the nose and mouth are detected in the face area with the previously learned results, and that the nose and mouth are not searched at the same time. There remains a need to conduct research on whether similar results can be obtained by running this program on Raspberry Pi to implement a device for checking standing masks, or by using CCTV instead of a PC camera.
  8. REFERENCES [1] Dong-Guen Kim, OpenCV Programming with Python, Kame Press,

    2018. [2] Youtube. Artificial intelligence that detects whether a mask is on or not [Internet] Available: https://www.youtube.com/watch?v=ncIyy1 doSJ8&t=66s [3] Github. [Internet] Available: https://github.com/peterbraden/node-open cv/tree/master/data Mask CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, infographics & images by Freepik