Image Based Recognition of Graze Direction Using Adaptive Methods

Human-machine interfaces based on gaze recognition can greatly simplify the handling of computer applications. However, most of the existing systems have problems with changing environments and different users. As a solution we use (i) adaptive components which can be trained online and (ii) detect common facial features, i.e. eyes, nose and mouth, for gaze recognition. In a first step an adaptive color histogram segmentation method roughly determines the region of interest including the user’s face. Within this region we then use a hierarchical recognition approach to detect the facial features. In the last stage of our system these feature positions are used to estimate gaze direction by detailed analysis of the eye region. We achieve an average precision of 1.5 ‡ for the gaze pan and 2.5 ‡ for the tilt angle while the user looks on a computer screen. The system runs at a rate of one frame per second on a common workstation.