Face Mask Detection using IoT and Raspberry Pi


Face Mask Detection using IoT and Raspberry Pi


In order to effectively prevent the spread of the COVID-19 virus, almost everyone wears a mask during the coronavirus epidemic. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train stations, etc. Therefore, it is very urgent to improve the recognition performance of the existing face recognition technology on masked faces. Most current advanced face recognition approaches are designed based on deep learning, which depends on a large number of face samples. However, at present, there are no publicly available masked face recognition datasets. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD), and Simulated Masked Face Recognition Dataset (SMFRD). Even though the primary objective is mask detection still the system can recognize each individual in the database based on face recognition. This can be used for an automatic attendance register, a raspberry pi is running the python script to demonstrate door opening and closing using a DC motor.? 

Face Mask Detection using IoT and Raspberry Pi


Face recognition is a promising area of applied computer vision. This technique is used to recognize a face or identify a person automatically from given images. In our daily life activities like, passport checking, smart door, access control, voter verification, criminal investigation, and many other purposes face recognition is widely used to authenticate a person correctly and automatically. Face recognition has gained much attention as a unique, reliable biometric recognition technology that makes it most popular than any other biometric technique likes password, pins, fingerprints, etc. Many governments across the world are also interested in the face recognition system to secure public places such as parks, airports, bus stations, and railway stations, etc. Face recognition is one of the well-studied real-life problems. Excellent progress has been done against face recognition technology Face Mask Detection using IoT and Raspberry Pi

Face Mask Detection using IoT and Raspberry Pi

Existing System:

  • Support Vector Machine
  • Discrete Wavelet Transform


  • Existing face recognition solutions are no longer reliable when wearing a mask.
  • Time-consuming Process
  • Poor Detection

Face Mask Detection using IoT and Raspberry Pi

Proposed System:

  • Convolution Neural Network-based face mask detection?
  • Caffe Models
  • OpenCV based face detection and recognition
  • Automatic attendance register?
  • Arduino Uno-based door controlling?


  • Highly Security
  • Its easy detection in a mask

Face Mask Detection using IoT and Raspberry Pi

Block diagram

Face Mask

Block diagram description

  • Most part of the system is implemented in python??
  • Only DC motor control is done using GPIO
  • The motor uses a driver to connect to raspberry pi and to get the power supply
  • L293D driver is used, the driver is interfaced using GPIO?

Hardware tools

  • Raspberry pi
  • DC motor
  • L293D driver
  • Camera?


Software Required:

  • Python Idle



  • Real-time Application We are using
  • Airports
  • Bus stops
  • Industries
  • Bus stops?.. etc.

Face Mask Detection using IoT and Raspberry Pi


[1] J. Deng, J. Guo, N. Xue, S. Zafeiriou, ?ArcFace: Additive Angular Margin Loss for Deep Face Recognition,? in CVPR, Jun. 2019, pp. 4685-4694.

[2] B. Liu, W. Deng, Y. Zhong, M. Wang, J. Hu, X. Tao, and Y. Huang, ?Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition?, in ICCV, Oct. 2019, pp. 10051-10060.

[3] W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, ?Sphereface: Deep hypersphere embedding for face recognition?in CVPR, Jul. 2017, pp. 6738-6746.

[4] W. Liu, Y. Wen, Z. Yu, and M. Yang, ?Large-margin softmax loss for convolutional neural networks?, in ICML, 2016, pp. 507-516.

[5] A. T. Tran, T. Hassner, I. Masi, and G. Medioni, ?Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network?, in CVPR, Jul. 2017, pp. 1493-1052.

[6] https://zhuanlan.zhihu.com/p/107719641?utm source=com.yin xing

[7] https://tzutalin.github.io/labelImg/

[8] http://dlib.net/

[9] G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller,?Labeled Faces in the wild: A database for studying face recognition in unconstrained environments?, Technical report, 2007.

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