Human Computer Interaction

David Geisler

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University of Tübingen
Dpt. of Computer Science
Human-Computer Interaction
Sand 14
72076 Tübingen
Germany

Telephone
+49 - (0) 70 71 - 29 - 70492
Telefax
+49 - (0) 70 71 - 29 - 50 62
E-Mail
david.geisler@uni-tuebingen.de
Office
Sand 14, C206
Office hours
on appointment

Research Interests

  • Autonomous driving and Driver Monitoring Technology
  • Gaze-based driver awareness and assistance systems
  • Visual Perception
  • Retinal scene processing

Publications

Encodji: Encoding Gaze Data Into Emoji Space for an Amusing Scanpath Classification Approach ;)

by Wolfgang Fuhl, Efe Bozkir, Benedikt Hosp, Nora Castner, David Geisler, Thiago C., and Enkelejda Kasneci

In Eye Tracking Research and Applications, 2019.

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Person Independent, Privacy Preserving, and Real Time Assessment of Cognitive Load using Eye Tracking in a Virtual Reality Setup

by E. Bozkir, D. Geisler, and E. Kasneci

In The IEEE Conference on Virtual Reality and 3D User Interfaces (VR) Workshops, 2019.

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The applicability of Cycle GANs for pupil and eyelid segmentation, data generation and image refinement

by W. Fuhl, D. Geisler, W. Rosenstiel, and E. Kasneci

In International Conference on Computer Vision Workshops, ICCVW, 2019.

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Assessment of Driver Attention During a Safety Critical Situation in VR to Generate VR-based Training

by E. Bozkir, D. Geisler, and E. Kasneci

In ACM Symposium on Applied Perception 2019, 2019.

[BIB]

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Real-time 3D Glint Detection in Remote Eye Tracking Based on Bayesian Inference

by David Geisler, Dieter Fox, and Enkelejda Kasneci

In International Conference on Robotics and Automation (ICRA), 2018.

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CBF:Circular binary features for robust and real-time pupil center detection

by W. Fuhl, D. Geisler, T. Santini, T. Appel, W. Rosenstiel, and E. Kasneci

In ACM Symposium on Eye Tracking Research & Applications, 2018.

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Development and Evaluation of a Gaze Feedback System Integrated into EyeTrace

by K. Otto, N. Castner, D. Geisler, and E. Kasneci

In Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (ETRA) , 2018.

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Saliency Sandbox: Bottom-Up Saliency Framework

by D. Geisler, W. Fuhl, T. Santini, and E. Kasneci

In 12th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), 2017.

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EyeRecToo: Open-Source Software for Real-Time Pervasive Head-Mounted Eye-Tracking

by T. Santini, W. Fuhl, D. Geisler, and E. Kasneci

In 12th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), 2017.

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EyeLad: Remote Eye Tracking Image Labeling Tool

by W. Fuhl, T. Santini, D. Geisler, T. C. Kübler, and E. Kasneci

In 12th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), 2017.

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On the necessity of adaptive eye movement classification in conditionally automated driving scenarios

by C. Braunagel, D. Geisler, W. Stolzmann, W. Rosenstiel, and E. Kasneci

In ACM Symposium on Eye Tracking Research & Applications, ETRA 2016, 2016.

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Eyes Wide Open? Eyelid Location and Eye Aperture Estimation for Pervasive Eye Tracking in Real-World Scenarios

by W. Fuhl, T. Santini, D. Geisler, T. C. Kübler, W. Rosenstiel, and E. Kasneci

In ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct publication – PETMEI 2016, 2016.

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Evaluation of State-of-the-Art Pupil Detection Algorithms on Remote Eye Images

by W. Fuhl, D. Geisler, T. Santini, and E. Kasneci

In ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct publication – PETMEI 2016, 2016.

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Research

Eye labeling tool

Ground truth data is an important prerequisite for the development and evaluation of many algorithms in the area of computer vision, especially when these are based on convolutional neural networks or other machine learning approaches that unfold their power mostly by supervised learning. This learning relies on ground truth data, which is laborious, tedious, and error prone for humans to generate. In this paper, we contribute a labeling tool (EyeLad) specifically designed for remote eye-tracking data to enable researchers to leverage machine learning based approaches in this field, which is of great interest for the automotive, medical, and human-computer interaction applications. The tool is multi platform and supports a variety of state-of-theart detection and tracking algorithms, including eye detection, pupil detection, and eyelid coarse positioning.

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Teaching

Course Term
Grundlagen der Multimediatechnik Winter 2019
Praktikum zu Grundlagen der Multimediatechnik Winter 2019
Technische Anwendungen der Informatik: Hard- und Software aktueller Eye-Tracking-Systeme Summer 2016
Seminar: Advanced Topics in Perception Engineering Summer 2019
Eye Tracking in Mobile Computing and Virtual Reality Winter 2016

News

New Website

The Perception Engineerung Group moves to a new website.

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