COVID-19: Face Mask Detection using TensorFlow and OpenCV Overview

Construct a CNN model to detect if a person is wearing a face mask or not with your webcam or mobile camera.

Covid-19 spread out, lock down ,What worse could you all have imagined than this? While everyone’s bored after being away from work/college for so long, I thought to share this project as an activity for ML Enthusiast!

I think all of you knows that it’s very important for us to wear mask before stepping out of our houses for getting our essentials. But in many areas people fail to do so and they ain’t get caught even. So to give a contribution to the society I decided to build a very simple and basic Convolutional Neural Network (CNN) model using TensorFlow with Keras library and OpenCV to detect if you are wearing a face mask to protect ourselves and others.

What you need to know about non-medical face masks for the whole ...

For building this project, I used a face mask dataset of my own.
So, basically I created another code through which I can take 50-100 of pictures at a time! The datasets had about pictures of which images of people were with mask and images without mask.

I am going to use these images to build a CNN model using TensorFlow to detect if you are wearing a face mask by using the webcam of your PC.

Step 1: Data Visualization

In the first step, let us visualize the total number of images in our dataset in both categories. I have taken around 700 images in the ‘With Mask’ class and 700 images in the ‘Without Mask’ class.

The number of images with facemask labelled 'yes': 700 
The number of images with facemask labelled 'no': 700

Step 2: Data Augmentation

In the next step, we augment our dataset to include more number of images for our training. In this step of data augmentation, we rotate and flip each of the images in our dataset. We see that, after data augmentation, we have a total of 2751 images with 1380 images in the ‘yes’ class and ‘1371’ images in the ‘no’ class.

Number of examples: 2751 
Percentage of positive examples: 50.163576881134134%, number of pos examples: 1380
Percentage of negative examples: 49.836423118865866%, number of neg examples: 1371

Step 3: Splitting the data

In this step, we split our data into the training set which will contain the images on which the CNN model will be trained and the test set with the images on which our model will be tested.

In this, we take split_size =0.8, which means that 80% of the total images will go to the training set and the remaining 20% of the images will go to the test set.

The number of images with facemask in the training set labelled 'yes': 1104
The number of images with facemask in the test set labelled 'yes': 276
The number of images without facemask in the training set labelled 'no': 1096
The number of images without facemask in the test set labelled 'no': 275

After splitting, we see that the desired percentage of images have been distributed to both the training set and the test set as mentioned above.

Step 4: Building the Model

In the next step, we build our Sequential CNN model with various layers such as Conv2D, MaxPooling2D, Flatten, Dropout and Dense. In the last Dense layer, we use the ‘softmax’ function to output a vector that gives the probability of each of the two classes.

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(100, (3,3), activation='relu', input_shape=(150, 150, 3)),
    tf.keras.layers.Conv2D(100, (3,3), activation='relu'),
    tf.keras.layers.Dense(50, activation='relu'),
    tf.keras.layers.Dense(2, activation='softmax')
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])

Here, we use the ‘adam’ optimizer and ‘binary_crossentropy’ as our loss function as there are only two classes. Additionally, you can even use the MobileNetV2 for better accuracy.

Image for post
CNN Model for Face Mask (Source — Self)

Step 5: Pre-Training the CNN model

After building our model, let us create the ‘train_generator’ and ‘validation_generator’ to fit them to our model in the next step. We see that there are a total of 2200 images in the training set and 551 images in the test set.

Found 2200 images belonging to 2 classes. 
Found 551 images belonging to 2 classes.

Step 6: Training the CNN model

This step is the main step where we fit our images in the training set and the test set to our Sequential model we built using keras library. I have trained the model for 30 epochs (iterations). However, we can train for more number of epochs to attain higher accuracy lest there occurs over-fitting.

history = model.fit_generator(train_generator,
callbacks=[checkpoint])>>Epoch 30/30
220/220 [==============================] - 231s 1s/step - loss: 0.0368 - acc: 0.9886 - val_loss: 0.1072 - val_acc: 0.9619

We see that after the 30th epoch, our model has an accuracy of 98.86% with the training set and an accuracy of 96.19% with the test set. This implies that it is well trained without any over-fitting.

Step 7: Labeling the Information

After building the model, we label two probabilities for our results. [‘0’ as ‘without_mask’ and ‘1’ as ‘with_mask’]. I am also setting the boundary rectangle color using the RGB values.[‘RED’ for ‘without_mask’ and ‘GREEN’ for ‘with_mask]


Step 8: Importing the Face detection Program

After this, we intend to use it to detect if we are wearing a face mask using our PC’s webcam. For this, first, we need to implement face detection. In this, I am using the Haar Feature-based Cascade Classifiers for detecting the features of the face.


This cascade classifier is designed by OpenCV to detect the frontal face by training thousands of images. The .xml file for the same needs to be downloaded and used in detecting the face. I have uploaded the file in my GitHub repository.

Step 9: Detecting the Faces with and without Masks

In the last step, we use the OpenCV library to run an infinite loop to use our web camera in which we detect the face using the Cascade Classifier. The code webcam = cv2.VideoCapture(0)denotes the usage of webcam.

The model will predict the possibility of each of the two classes ([without_mask, with_mask]). Based on which probability is higher, the label will be chosen and displayed around our faces.

Additionally, you can download the DroidCam application for both Mobile and PC to use your mobile’s camera and change the value from 0 to 1 in webcam= cv2.VideoCapture(1).


From the above demo video, we see that the model was correctly able to detect if I was wearing a mask or not. You will see in the video it is also detecting my face when I am not wearing the mask and then triggering the alert to my phone about the warning and suggesting the nearest store where I can get the face mask. In coming tutorial I will be sharing how to detect face and trigger the message and integrate all together.

Check Out Face Detection For Beginner Tutorial Here

Check Out Face Detection For Beginner Tutorial Here

GitHub Repository:

Data Set:

In this article, we have successfully over viewed a CNN model to detect if a person is wearing a face mask or not.

To make an advanced and more accurate Mask detection model

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