Face Mask Detection using CNN with OpenCV


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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, a 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, pin, fingerprint, etc. Many of governments across the world 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

Project Overview:

Face mask detection is a simple model to detect face mask. Due to COVID-19, there is a need to face mask detection applications in many places like Malls and Theatres for safety. By the development of face mask detection, we can detect if the person is wearing a face mask, and allow their entry would be of great help to society. Face Mask detection model is built using the Deep Learning technique called Convolutional Neural Networks (CNN). This CNN Model is built using the Keras and Tensor Flow framework and the OpenCV library which is highly used for real-time applications.

System Analysis:

The face mask recognition system uses Opencv to detect the person with or without a mask. It can be connected with any surveillance system installed at your premises. The authorities or admin can check the person through the system to confirm their identity. The system sends an alert message to the authorized person if someone has entered the premises without a face mask. The accuracy rate of detecting a person with a face mask is 95-97% depending on the digital capabilities. The data has been transferred and stored automatically in the system to enable reports whenever you want.


Existing System:

  • Support Vector Machine
  • Discrete Wavelet Transform

Proposed System:

  • Convolution Neural Network
  • Caffe Models

Hardware Specification:

  • Windows Os
  • Minimum 2GB RAM

Software Specification:

  • Python Idle
  • Anaconda Navigator

System Design:

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