Generate Haar Feature for Face Detection Without Mask and Face with Mask

ABSTRACT

The The COVID-19 pandemic is a problem that worries the wider community. To stop the spread of COVID-19, the mandatory health protocol to use masks is enforced. Many people do not comply with these health protocols. Based on these problems, a technology was developed to monitor the face that uses a mask or not used. This technology uses the Viola-Jones algorithm. In carrying out its detection function, this algorithm requires a classifier which is the result of training on some positive and negative image data sets. In this study, two positive image data sets were used facial data using masks and facial data not using masks. The classifier obtained from the training process is a cascade file in XML format that will be used in the detection program. In this study, several training processes were carried out to obtain a good comparison value between positive and negative dataset samples in forming a cascade. The cascade test with the highest accuracy value was obtained from the classifier using 1000 positive samples and 2000 negative samples, namely 98.70% for face detection without a mask and 92.63% for face detection using a mask.