Towards Hand-Over-Face Gesture Detection

Abstract

Facial microexpressions are immediately appearing reactions on the face that indicate various details about people's mental and emotional states. Their most important property is that their interpretation is identical or very similar for people all over the world. At present, their identification requires a psychologist expert. Thus automating this task would enable a broader application. The goal of this research is the detection of microexpressions using hybrid expert algorithms. Our algorithms mainly rely on landmark point detectors. Based on their output, several expert algorithms are utilized to extract key features and changes appearing on the face of a subject. These algorithms usually include several steps of image processing and time series analysis algorithms. In this paper, a component responsible for detecting hand gestures and hand pose is introduced. This component helps other algorithms to eliminate false positive detections by detecting the hands over the face. In addition, the recognizability of hand-over-face gestures is investigated. Finally, the implemented face occlusion detector method is evaluated on videos.