Eye Detection from Face Images covered in Face-masks using HAAR features

The contagious illness known as COVID-19 made wearing a mask an essential part of daily life. Mask-covered faces cannot be detected by the current eye detection methods. Many biometric identification systems, like iris recognition, depend on accurate eye detection. Thus, in this study, an efficient method using machine learning for detecting eyes of people wearing mask is presented. Haar-cascade classifier is used to implement real-time eye detection from a live stream via webcam. From the live stream, frames are extracted and saved as images. Dataset was prepared by collecting face images of people wearing mask under various background. Haar-cascade classifier which was trained using 2000 positive and 4000 negative images is used to detect the position of eyes. According to the results on dataset, the system could attain an average accuracy of 96.72%.