David Geisler
Papers
1
Total Citations
5
H-Index
1
About
David Geisler is a researcher at the forefront of human-robot interaction and computer vision, with a specialized focus on advancing remote eye-tracking technologies. His key research areas include gaze estimation, 3D glint detection, and calibration-free methods for human-machine interfaces. Geisler's most notable contribution is his work on "Real-time 3D Glint Detection in Remote Eye Tracking Based on Bayesian Inference" (2018, 5 citations), where he developed a robust, probabilistic approach to detecting corneal reflections in real-time—a critical step toward enabling natural, non-intrusive gaze-based interaction. By addressing the challenge of reliable gaze estimation without user calibration, his research directly supports the vision of fluent human-robot collaboration, allowing robots to interpret human cognitive states and intentions through eye movements. Though his citation count is modest, the work is foundational in pushing the boundaries of practical, real-world eye tracking. Geisler's achievements lie in bridging Bayesian inference with real-time computer vision, offering a scalable solution that could transform how humans interact with autonomous systems. His contributions are particularly relevant for researchers exploring intuitive interfaces and socially aware robotics.
Research Focus
Key Achievements
Top Papers
- 1