Mask Wearing Detection Using OpenCV Training Data

https://aaron.kr/content/portfolio/can-we-detect-mask-wearing-with-a-pc-webcam/

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.

Transcript

1. OpenCV 학습 데이터를 이용한 마스크 착용 감지
   Mask Wearing Detection
   Using OpenCV Training Data
   Aaron Daniel Snowberger,
   Choong Ho Lee
   Mask
   No Mask
   

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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.
   

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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
   

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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
   

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5. IMPLEMENTATION & RESULTS
   WITH MASK
   WITHOUT MASK
   If eyes, 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
   

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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
   

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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.
   

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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
   

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