David Geisler

Learn More
Eyelid identification and aperture estimation provide key data that can be used to infer valuable information about a subject's mental state (e.g., vigilance, fatigue, and drowsiness) as well as validate or reduce the search space of other eye features. In this paper, we consider these tasks from the perspective of pervasive eye tracking, taking into(More)
Eye movements are a powerful source of information as well as the most intuitive form of interaction. Although eye-tracking technology is still in its infancy, it offers the greatest potential for novel communication solutions and applications. Whereas head-mounted eye-trackers are widely used in research, several applications require most unintrusive eye(More)
Head-mounted eye tracking offers remarkable opportunities for research and applications regarding pervasive health monitoring, mental state inference, and human computer interaction in dynamic scenarios. Although a plethora of software for the acquisition of eye-tracking data exists, they often exhibit critical issues when pervasive eye tracking is(More)
Algorithms for eye movement classification are separated into threshold-based and probabilistic methods. While the parameters of static threshold-based algorithms usually need to be chosen for the particular task (task-individual), the probabilistic methods were introduced to meet the challenge of adjusting automatically to multiple individuals with(More)
  • 1