ABSTRACT
Humans around the world are affected by special infectious pneumonia (COVID-19). There are more and more people wearing masks that are necessary for daily or workplace use. However, the sensitivity of face detection will be affected by feature obscuration, and most of them cannot be performed. Obscured face detection and gaze tracking. This paper proposes a face detection and landmark repair, and then realizes the tracking of the eye trajectory of the obscured face. Model database with obscured face image data can also include unobscured face image data. After calibrated eye area, machine learning [1] algorithm is used for model database training to achieve eye area detection and provide real-time position coordinates. The eye information of the partial simulation model is superimposed and calculated to complete the feature point restoration, feature point detection and definition. Finally, K-means [2] is used to classify the image around the eyes to distinguish the eyeball from the white of the eye and calculate the position of the eyeball center. The face wearing a mask will affect the sensitivity of face detection, and the person wearing a mask cannot be detected. We use a two-stage method to locate the eyeball center of the face wearing a mask. We use the machine learning algorithm to detect the bounding box near the eyes, and we use the obscured image to train our model. Then attach the chin pattern to the place that is expected to be covered. Use a general cross-platform machine learning library [3] to locate area near the eyeball. Then use an unsupervised learning clustering algorithm to classify the image near the eyeball to analyze the eyeball area and find the center of the eyeball, to achieve the purpose of eye tracking. © 2020 ITE and SID.