Face and Face Mask Detection Using Convolutional Neural Network

The COVID-19 outbreak has posed a severe healthcare concern in Malaysia. Wearing a mask is the most effective way to prevent infections. However, some Malaysians refuse to wear a face mask for a variety of reasons. This work proposes a real-time face and face mask detection method using image processing technique to promote wearing face mask. Haar Cascade is used for the face detection to extract the features of the human faces as a method of approach. On the other hand, the face mask detection utilizes convolutional neural network (CNN) to train a model using the MobileNetV2 training model designed using Python, Keras and Tensorflow. OpenCV package was used as the interface for the algorithms to be connected to a web camera. Based on the performance metric calculation of detection rate analysis of the experimental results, the face detection rate is at 90% true and 10% false detection, which shows very good detection rate. Furthermore, the training accuracy and validation accuracy for the face mask detector are efficiently near to 1.0, proving a steady accuracy over the time. Training loss and validation loss are almost near to zero and decreasing over time, reassuring the algorithm performance is accurate and efficient for a datasets of 4000 images.