a4vg / facemask-detection

Detect if a person is wearing a facemask. CNN model created build with TensorFlow and Keras, camera and face detection with OpenCV.

Face Mask Detection

Detect face with and without a facemask. CNN model created build with TensorFlow and Keras, camera and face detection with OpenCV.

A detailed description of the project can be found at the Jupyter notebook informe.ipynb.

Run

Requirements:

• python-opencv
• numpy
• tensorflow

python detector.py

Results

The project includes a ready-to-use CNN Tensorflow model (out/cnn.hdf5) trained, tested and validated with this dataset (not included here to save space).

CNN Model

Keras layers:

Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=(50,50,3))
Conv2D(64, kernel_size=(3,3), activation='relu')
MaxPooling2D( pool_size=(2,2) )

Flatten()
Dense(64, activation='relu')
Dense(20, activation='relu')
Dense(2, activation='softmax')

Optimizer: Gradient descent with 0.05 as learning rate (tensorflow.keras.optimizers.SGD(lr=0.05))

Training:

Parameters:

• epochs: 10
• Image sizes: Resized to 50x50
• Training data: 10 000 images
• Test data: 992 images

Validation metrics

The model was validated with 800 images in the dataset and the metrics were obtained with sklearn.metrics.classification_report.

              precision    recall  f1-score   support

 WithoutMask       1.00      0.99      1.00       400
    WithMask       1.00      1.00      1.00       400

    accuracy                           1.00       800
   macro avg       1.00      1.00      1.00       800
weighted avg       1.00      1.00      1.00       800