Prof. Dr. Enkelejda Kasneci
University of Tübingen
Dpt. of Computer Science
Human-Computer Interaction
Sand 14
72076 Tübingen
Germany
- Telephone
- +49 - (0) 70 71 - 29 - 74015
- Telefax
- +49 - (0) 70 71 - 29 - 50 62
- enkelejda.kasneci@uni-tuebingen.de
- Office
- Sand 14, C221
- Office hours
- on appointment
Enkelejda Kasneci is a Professor of Computer Science at the University of Tübingen, Germany, where she leads the Human-Computer Interaction Lab. As a BOSCH scholar, she received her M.Sc. degree in Computer Science from the University of Stuttgart in 2007. In 2013, she received her PhD in Computer Science from the University of Tübingen. For her PhD research, she was awarded the research prize of the Federation Südwestmetall in 2014. From 2013 to 2015, she was a postdoctoral researcher and a Margarete-von-Wrangell Fellow at the University of Tübingen. Her research evolves around the application of machine learning for intelligent and perceptual human-computer interaction. She served as academic editor for PlosOne and as a TPC member and reviewer for several major conferences and journals.
Research Interests
- Eye-tracking methods and applications, especially eye tracking in the wild
- Applied machine learning
- Eye movements and driving
- Autonomous driving and Driver Observation Technology
- Eye movements and VR/AR
Invited Talks
- 2020: ECCV 2020 OpenEyes: Eye Gaze in AR, VR and in the wild
- 2020: Closing Keynote at Augmented Human, AH 2020
- 2019: Machine Learning in Education, FernUni Hagen
- 2019: Keynote at the ACM Symposium on Eye Tracking Research and Applications, ETRA 2019
- 2019: Opening Keynote European Conference on Eye Movements, ECEM 2019
- 2018: Keynote on Cognitive Interfaces, Ada-Lovelace-Festival, Berlin
- 2018: Global Female Leaders Summit, Berlin
- 2017: Hub.Berlin
- 2017: Ada-Lovelace-Festival, Berlin
- 2017: Eye-Tracking während des Fahrens, OCG Jahresopening 2017, Vienna
- 2016: It’s in your eyes – How eye tracking will shape our future, Ada-Lovelace-Festival, Berlin
- 2016: Maschinelles Lernen und Eye-Tracking-Technologie zur Erforschung der Mechanismen der visuellen Wahrnehmung, INFORMATIK2016, Klagenfurt
- 2015: Eye tracking in natural settings - Challenges and opportunities, Institut für Neuro- und Bioinformatik, Universität zu Lübeck
- 2015: Arbitrarily shaped areas of interest based on gaze density gradient, European Conference on Eye Movements, ECEM 2015, Vienna
- 2015: Exploiting the potential of eye movements analysis in the driving context, Perceptual User Interfaces Group, Max Planck Institute for Informatics, Saarbrücken
- 2015: Eye movements and driving, Volkswagen Research Center, Wolfsburg
- 2014: Online Eye-Tracking Data Analysis, SMI Vision, Research Center Berlin
- 2013: Towards the Automated Recognition of Assistance Need for Drivers with Impaired Visual Field, Mercedes-Benz Technology Center Sindelfingen
Scholarships, Awards and Administrative Functions
- 2020 Ruf auf eine W3 Professur an der TU Chemnitz (abgelehnt)
- Since 2019 Full member of DFG Cluster of Excellence - Machine Learning of Science
- since 2016 Associated Member of the Learning, Educational Achievement, and Life Course Development (LEAD), Graduate School and Research Network
- since 2016 Junior Fellow of the German Informatics Society (Gesellschaft für Informatik e.V. (GI))
- since 2016 Fast Track program of the Robert Bosch Stiftung (Excellence and Leadership Skills for Outstanding Women in Science)
- 2016 NVIDIA Hardware Grant
- 2014 - 2015 Margarete-von-Wrangell-Fellowship
- 2014 Südwestmetall-Förderpreis 2014 für Nachwuchswissenschaftler
- 2001-2007 Scholarship of the Robert-Bosch-Stiftung
Publications
2020
Predicting visual perceivability of scene objects through spatio-temporal modeling of retinal receptive fields
David Geisler, Andrew T Duchowski, and Enkelejda Kasneci. Neurocomputing. Elsevier, 2020.
Distilling Location Proposals of Unknown Objects Through Gaze Information for Human-Robot Interaction
Daniel Weber, Thiago Santini, Andreas Zell, and Enkelejda Kasneci. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020.
RemoteEye: An open-source high-speed remote eye tracker
Benedikt Hosp, Shahram Eivazi, Maximilian Maurer, Woflgang Fuhl, David Geisler, and Enkelejda Kasneci. Behavior Research Methods, pages 1–15. Springer, 2020.
Deep semantic gaze embedding and scanpath comparison for expertise classification during OPT viewing
Nora Castner, Thomas C Kübler, Juliane Richter, Therese Eder, Fabian Huettig, Constanze Keutel, and Enkelejda Kasneci. Eye Tracking Research and Applications. ACM, 2020.
Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework
Efe Bozkir, Ali B. Ünal, Mete Akgün, Enkelejda Kasneci, and Nico Pfeifer. Eye Tracking Research and Applications. ACM, 2020.
A Novel -Eye-Tracking Sensor for AR Glasses Based on Laser Self-Mixing Showing Exceptional Robustness Against Illumination
Johannes Meyer, Thomas Schlebusch, Hans Spruit, Jochen Hellmig, and Enkelejda Kasneci. Eye Tracking Research and Applications. ACM, 2020.
Exploiting the GBVS for Saliency aware Gaze Heatmaps
David Geisler, Daniel Weber, Nora Castner, and Enkelejda Kasneci. Eye Tracking Research and Applications. ACM, 2020.
A MinHash approach for fast scanpath classification
David Geisler, Nora Castner, Gjergji Kasneci, and Enkelejda Kasneci. Eye Tracking Research and Applications. ACM, 2020.
Training Decision Trees as Replacement for Convolution Layers
W. Fuhl, G. Kasneci, W. Rosenstiel, and E. Kasneci. Conference on Artificial Intelligence, AAAI, 2020.
Tiny convolution, decision tree, and binary neuronal networks for robust and real time pupil outline estimation
W. Fuhl, H. Gao, and E. Kasneci. ACM Symposium on Eye Tracking Research & Applications, ETRA 2020. ACM, 2020.
Neural networks for optical vector and eye ball parameter estimation
W. Fuhl, H. Gao, and E. Kasneci. ACM Symposium on Eye Tracking Research & Applications, ETRA 2020. ACM, 2020.
A Novel Camera-Free Eye Tracking Sensor for Augmented Reality based on Laser Scanning
Johannes Meyer, Thomas Schlebusch, Wolfgang Fuhl, and Enkelejda Kasneci. Sensors Journal, pages 1-1. IEEE, 2020.
Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction
Wolfgang Fuhl, Yao Rong, and Kasneci Enkelejda. Proceedings of the International Conference on Pattern Recognition, pages 0–0, 2020.
Explainable Online Validation of Machine Learning Models for Practical Applications
Wolfgang Fuhl, Yao Rong, Thomas Motz, Michael Scheidt, Andreas Hartel, Andreas Koch, and Enkelejda Kasneci. Proceedings of the International Conference on Pattern Recognition, pages 0–0, 2020.
Multi Layer Neural Networks as Replacement for Pooling Operations
Wolfgang Fuhl and Enkelejda Kasneci. arXiv preprint arXiv:2006.06969. CoRR, 2020.
Reinforcement learning for the privacy preservation and manipulation of eye tracking data
Wolfgang Fuhl, Efe Bozkir, and Enkelejda Kasneci. arXiv preprint arXiv:2002.06806. CoRR, 2020.
Eye Tracking Data Collection Protocol for VR for Remotely Located Subjects using Blockchain and Smart Contracts
Efe Bozkir, Shahram Eivazi, Mete Akgün, and Enkelejda Kasneci. IEEE International Conference on Artifical Intelligence and Virtual Reality (AIVR), – To Appear in Work-in-Progress papers, 2020.
Differential Privacy for Eye Tracking with Temporal Correlations
Efe Bozkir, Onur Günlü, Wolfgang Fuhl, Rafael F. Schaefer, and Enkelejda Kasneci. arXiv preprint arXiv:2002.08972. CoRR, 2020.
Weight and Gradient Centralization in Deep Neural Networks
Wolfgang Fuhl and Enkelejda Kasneci. arXiv. CoRR, 2020.
Rotated Ring, Radial and Depth Wise Separable Radial Convolutions
Wolfgang Fuhl and Enkelejda Kasneci. arXiv. CoRR, 2020.
Pupil diameter differentiates expertise in dental radiography visual search
Nora Castner, Tobias Appel, Thérése Eder, Juliane Richter, Katharina Scheiter, Constanze Keutel, Fabian Hüttig, Andrew Duchowski, and Enkelejda Kasneci. PLOS ONE 15(5): 1-19. Public Library of Science, 2020.
Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring
Rong Yao, Akata Zeynep, and Kasneci Enkelejda. IEEE Conference on Intelligent Transportation Systems (ITSC), 2020, pages 0–0, 2020.
The display makes a difference: A mobile eye tracking study on the perception of art before and after a museum’s rearrangement
L. Reitstätter, H. Brinkmann, T. Santini, E. Specker, Z. Dare, F. Bakondi, A. Miscená, E. Kasneci, H. Leder, and R. Rosenberg. Peer-reviewed publication, 2020.
Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm
Mariella Dreißig, Mohamed Baccour, Tim Schäck, and Enkelejda Kasneci. Peer-reviewed publication. IEEE, 2020.
2019
Encodji: Encoding Gaze Data Into Emoji Space for an Amusing Scanpath Classification Approach ;)
Wolfgang Fuhl, Efe Bozkir, Benedikt Hosp, Nora Castner, David Geisler, Thiago C., and Enkelejda Kasneci. Eye Tracking Research and Applications, 2019.
Person Independent, Privacy Preserving, and Real Time Assessment of Cognitive Load using Eye Tracking in a Virtual Reality Setup
Efe Bozkir, David Geisler, and Enkelejda Kasneci. The IEEE Conference on Virtual Reality and 3D User Interfaces (VR) Workshops, 2019.
Improving Real-Time CNN-Based Pupil Detection Through Domain-Specific Data Augmentation
S. Eivazi, T. Santini, A. Keshavarzi, T. C. Kübler, and A. Mazzei. Proceedings of the 2019 ACM Symposium on Eye Tracking Research & Applications (ETRA) – To Appear, 2019.
Get a Grip: Slippage-Robust and Glint-Free Gaze Estimation for Real-Time Pervasive Head-Mounted Eye Tracking
T. Santini, D. Niehorster, and E. Kasneci. Proceedings of the 2019 ACM Symposium on Eye Tracking Research & Applications (ETRA), 2019.
Ferns for area of interest free scanpath classification
W. Fuhl, N. Castner, T. C. Kübler, A. Lotz, W. Rosenstiel, and E. Kasneci. Proceedings of the 2019 ACM Symposium on Eye Tracking Research & Applications (ETRA) , 2019.
500,000 images closer to eyelid and pupil segmentation
W. Fuhl, W. Rosenstiel, and E. Kasneci. Computer Analysis of Images and Patterns, CAIP, 2019.
The applicability of Cycle GANs for pupil and eyelid segmentation, data generation and image refinement
W. Fuhl, D. Geisler, W. Rosenstiel, and E. Kasneci. International Conference on Computer Vision Workshops, ICCVW, 2019.
Learning to validate the quality of detected landmarks
W. Fuhl and E. Kasneci. International Conference on Machine Vision, ICMV, 2019.
Assessment of Driver Attention During a Safety Critical Situation in VR to Generate VR-based Training
Efe Bozkir, David Geisler, and Enkelejda Kasneci. ACM Symposium on Applied Perception 2019, 2019.
2018
Eye-Hand Behavior in Human-Robot Shared Manipulation
R. M., T. Santini, T. C. Kübler, E. Kasneci, S. Srinivasa, and H. Admoni. Proceedings of the 13th Annual ACM/IEEE International Conference on Human Robot Interaction, 2018.
Real-time 3D Glint Detection in Remote Eye Tracking Based on Bayesian Inference
David Geisler, Dieter Fox, and Enkelejda Kasneci. International Conference on Robotics and Automation (ICRA), 2018.
PuRe: Robust Pupil Detection for Real-Time Pervasive Eye Tracking
T. Santini, W. Fuhl, and E. Kasneci. Elsevier Computer Vision and Image Understanding To Appear, 2018.
The Art of Pervasive Eye Tracking: Unconstrained Eye Tracking in the Austrian Gallery Belvedere
T. Santini, H. Brinkmann, L. Reistätter, H. Leder, R. Rosenberg, W. Rosenstiel, and E. Kasneci. Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (ETRA): Adjunct publication – PETMEI 2018, 2018.
PuReST: Robust Pupil Tracking for Real-Time Pervasive Eye Tracking
T. Santini, W. Fuhl, and E. Kasneci. Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (ETRA), 2018.
Overlooking: The nature of gaze behavior and anomaly detection in expert dentists
Nora Castner, Solveig Klepper, Lena Kopnarski, Fabian Hüttig, Constanze Keutel, Katharina Scheiter, Juliane Richter, T. Eder, and Enkelejda Kasneci. Workshop on Modeling Cognitive Processes from Multimodal Data (MCPMD’18 ), 2018.
CBF:Circular binary features for robust and real-time pupil center detection
W. Fuhl, D. Geisler, T. Santini, T. Appel, W. Rosenstiel, and E. Kasneci. ACM Symposium on Eye Tracking Research & Applications, 2018.
Teachers’ Perception in the Classroom
Ö. Sümer, P. Goldberg, K. Stürmer, T. Seidel, P. Gerjets, U. Trautwein, and E. Kasneci. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018.
Development and Evaluation of a Gaze Feedback System Integrated into EyeTrace
K. Otto, N. Castner, D. Geisler, and E. Kasneci. Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (ETRA) , 2018.
An Inconspicuous and Modular Head-Mounted Eye Tracker
S. Eivazi, T. Kübler, T. Santini, and E. Kasneci. Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (ETRA) , 2018.
Automatic generation of saliency-based areas of interest
W. Fuhl, T. Kübler, T. Santini, and E. Kasneci. Symposium on Vision, Modeling and Visualization (VMV), 2018.
Region of interest generation algorithms for eye tracking data
W. Fuhl, T. C. Kübler, H. Brinkmann, R. Rosenberg, W. Rosenstiel, and E. Kasneci. Third Workshop on Eye Tracking and Visualization (ETVIS), in conjunction with ACM ETRA, 2018.
Scanpath comparison in medical image reading skills of dental students
N. Castner, E. Kasneci, T. C. Kübler, K. Scheiter, and J. Richter. Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (ETRA), 2018.
MAM: Transfer learning for fully automatic video annotation and specialized detector creation
W. Fuhl, N. Castner, L. Zhuang, M. Holzer, W. Rosenstiel, and E. Kasneci. International Conference on Computer Vision Workshops, ICCVW, 2018.
Eye movement velocity and gaze data generator for evaluation, robustness testing and assess of eye tracking software and visualization tools
W. Fuhl and E. Kasneci. Poster at Egocentric Perception, Interaction and Computing, EPIC, 2018.
BORE: Boosted-oriented edge optimization for robust, real time remote pupil center detection
W. Fuhl, S. Eivazi, B. Hosp, A. Eivazi, W. Rosenstiel, and E. Kasneci. Eye Tracking Research and Applications, ETRA, 2018.
Rule based learning for eye movement type detection
W. Fuhl, N. Castner, and E. Kasneci. International Conference on Multimodal Interaction Workshops, ICMIW, 2018.
Histogram of oriented velocities for eye movement detection
W. Fuhl, N. Castner, and E. Kasneci. International Conference on Multimodal Interaction Workshops, ICMIW, 2018.
Eye movement simulation and detector creation to reduce laborious parameter adjustments
W. Fuhl, T. Santini, T. Kuebler, N. Castner, W. Rosenstiel, and E. Kasneci. arXiv preprint arXiv:1804.00970, 2018.
2017
Online recognition of driver-activity based on visual scanpath classification
Christian Braunagel, David Geisler, Wolfgang Rosenstiel, and Enkelejda Kasneci. IEEE Intelligent Transportation Systems Magazine 9(4): 23–36. IEEE, 2017.
Eye tracking as a tool to evaluate functional ability in everyday tasks in glaucoma
E. Kasneci, A. A., and J. M.. Journal of Ophthalmology Article ID 6425913, 2017.
Saliency Sandbox: Bottom-Up Saliency Framework
D. Geisler, W. Fuhl, T. Santini, and E. Kasneci. 12th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), 2017.
EyeRecToo: Open-Source Software for Real-Time Pervasive Head-Mounted Eye-Tracking
T. Santini, W. Fuhl, D. Geisler, and E. Kasneci. 12th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), 2017.
EyeLad: Remote Eye Tracking Image Labeling Tool
W. Fuhl, T. Santini, D. Geisler, T. C. Kübler, and E. Kasneci. 12th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), 2017.
Fast and Robust Eyelid Outline and Aperture Detection in Real-World Scenarios
W. Fuhl, T. Santini, and E. Kasneci. IEEE Winter Conference on Applications of Computer Vision (WACV 2017), 2017.
Aggregating physiological and eye tracking signals to predict perception in the absence of ground truth
E. Kasneci, T. C. Kübler, K. Broelemann, and G. Kasneci. Computers in Human Behavior, Elsevier 68: 450-455, 2017.
Ways of improving the precision of eye tracking data: Controlling the influence of dirt and dust on pupil detection
W. Fuhl, T. C. Kübler, D. Hospach, O. Bringmann, W. Rosenstiel, and E. Kasneci. Journal of Eye Movement Research 10(3), 2017.
CalibMe: Fast and Unsupervised Eye Tracker Calibration for Gaze-Based Pervasive Human-Computer Interaction
T. Santini, W. Fuhl, and E. Kasneci. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017.
Towards pervasive eye tracking
E. Kasneci. it-Information Technology, De Gruyter Oldenbourg, 2017.
Using Eye Tracking to Evaluate and Develop Innovative Teaching Strategies for Fostering Image Reading Skills of Novices in Medical Training
N. Castner, S. Eivazi, K. Scheiter, and E. Kasneci. Eye Tracking Enhanced Learning (ETEL2017), 2017.
Automatic Mapping of Remote Crowd Gaze to Stimuli in the Classroom
T. Santini, T. C. Kübler, L. Draghetti, P. Gerjets, W. Wagner, U. Trautwein, and E. Kasneci. Eye Tracking Enhanced Learning (ETEL2017), 2017.
Towards Intelligent Surgical Microscopes: Surgeons Gaze and Instrument Tracking
Shahram Eivazi, Wolfgang Fuhl, and Enkelejda Kasneci. Proceedings of the 22st International Conference on Intelligent User Interfaces, IUI 2017. ACM, 2017.
Towards automatic skill evaluation in microsurgery
Shahram Eivazi, Michael Slupina, Wolfgang Fuhl, Hoorieh Afkari, Ahmad Hafez, and Enkelejda Kasneci. Proceedings of the 22st International Conference on Intelligent User Interfaces, IUI 2017. ACM, 2017.
Monitoring Response Quality During Campimetry Via Eye-Tracking
G. Dambros, J. Ungewiss, T. C. Kübler, E. Kasneci, and M. Spüler. Proceedings of the 22st International Conference on Intelligent User Interfaces, IUI 2017, ACM, 2017.
PupilNet v2.0: Convolutional Neural Networks for Robust Pupil Detection
W. Fuhl, T. Santini, G. Kasneci, and E. Kasneci. CoRR, 2017.
Fast camera focus estimation for gaze-based focus control
W. Fuhl, T. Santini, and E. Kasneci. CoRR, 2017.
Optimal eye movement strategies: a comparison of neurosurgeons gaze patterns when using a surgical microscope
S. Eivazi, A. Hafez, W. Fuhl, H. Afkari, E. Kasneci, M. Lehecka, and R. Bednarik. Acta Neurochirurgica, 2017.
2016
EyeRec: An Open-source Data Acquisition Software for Head-mounted Eye-tracking
T. Santini, W. Fuhl, T. C. Kübler, and E. Kasneci. Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) 3: VISAPP: 386–391, 2016.
3D Gaze Estimation Using Eye Vergence
E.G. Mlot, H. Bahmani, S. Wahl, and E. Kasneci. 9th International Conference on Health Informatics, Healthinf 2016, 2016.
Rendering refraction and reflection of eyeglasses for synthetic eye tracker images
T. C. Kübler, T. Rittig, J. Ungewiss, C. Krauss, and E. Kasneci. ACM Symposium on Eye Tracking Research and Applications, ETRA 2016, 2016.
On the necessity of adaptive eye movement classification in conditionally automated driving scenarios
C. Braunagel, D. Geisler, W. Stolzmann, W. Rosenstiel, and E. Kasneci. ACM Symposium on Eye Tracking Research & Applications, ETRA 2016, 2016.
ElSe: Ellipse Selection for Robust Pupil Detection in Real-World Environments
W. Fuhl, T. Santini, T. C. Kübler, and E. Kasneci. Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications (ETRA), pages 123–130, 2016.
Bayesian Identification of Fixations, Saccades, and Smooth Pursuits
T. Santini, W. Fuhl, T. C. Kübler, and E. Kasneci. Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications (ETRA), pages 163–170, 2016.
Pupil detection for head-mounted eye tracking in the wild: An evaluation of the state of the art
Wolfgang Fuhl, Marc Tonsen, Andreas Bulling, and Enkelejda Kasneci. Machine Vision and Applications, pages 1-14, 2016.
SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies
T. C. Kübler, C. Rothe, U. Schiefer, W. Rosenstiel, and E. Kasneci. Behavior Research Methods online first: 1-17, 2016.
Eyes Wide Open? Eyelid Location and Eye Aperture Estimation for Pervasive Eye Tracking in Real-World Scenarios
W. Fuhl, T. Santini, D. Geisler, T. C. Kübler, W. Rosenstiel, and E. Kasneci. ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct publication – PETMEI 2016, 2016.
Brightness- and Motion-Based Blink Detection for Head-Mounted Eye Trackers
T. Appel, T. Santini, and E. Kasneci. ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct publication – PETMEI 2016, 2016.
Novel methods for analysis and visualization of saccade trajectories
T. C. Kübler, W. Fuhl, R. Rosenberg, W. Rosenstiel, and E. Kasneci. 3. ECCV Workshop VISART 2016, 2016.
Non-Intrusive Practitioner Pupil Detection for Unmodified Microscope Oculars
W. Fuhl, T. Santini, C. Reichert, D. Claus, A. Herkommer, H. Bahmani, K. Rifai, S. Wahl, and E. Kasneci. Elsevier Computers in Biology and Medicine 79: 36-44, 2016.
Evaluation of State-of-the-Art Pupil Detection Algorithms on Remote Eye Images
W. Fuhl, D. Geisler, T. Santini, and E. Kasneci. ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct publication – PETMEI 2016, 2016.
Feature-based attentional influences on the accommodation response
H. Bahmani, W. Fuhl, E. Gutierrez, G. Kasneci, E. Kasneci, and S. Wahl. Vision Sciences Society Annual Meeting Abstract, 2016.
PupilNet: Convolutional Neural Networks for Robust Pupil Detection
W. Fuhl, T. Santini, G. Kasneci, and E. Kasneci. CoRR, 2016.
2015
Towards Automated Scan Pattern Analysis for Dynamic Scenes
J. Ungewiss, T. C. Kübler, D.R. Bukenberger, E. Kasneci, and U. Schiefer. The Eye, The Brain And The Auto 2015, 2015.
Online Recognition of Fixations, Saccades, and Smooth Pursuits for Automated Analysis of Traffic Hazard Perception
E. Kasneci, G. Kasneci, T. C. Kübler, and W. Rosenstiel. 4: 411-434. Artificial Neural Networks - Springer Series in Bio-/Neuroinformatics (Ed. Petia Koprinkova-Hristova, Valeri Mladenov, Nikola K. Kasabov), Springer International Publishing, 2015.
Analysis of eye movements with Eyetrace
T. C. Kübler, K. Sippel, W. Fuhl, G. Schievelbein, J. Aufreiter, R. Rosenberg, W. Rosenstiel, and E. Kasneci. 574: 458-471. Biomedical Engineering Systems and Technologies. Communications in Computer and Information Science (CCIS). Springer International Publishing, 2015.
Exploiting the potential of eye movements analysis in the driving context
E. Kasneci, T. C. Kübler, C. Braunagel, W. Fuhl, W. Stolzmann, and W. Rosenstiel. 15. Internationales Stuttgarter Symposium Automobil- und Motorentechnik. Springer Fachmedien Wiesbaden, 2015.
ExCuSe: Robust Pupil Detection in Real-World Scenarios
W. Fuhl, T. C. Kübler, K. Sippel, W. Rosenstiel, and E. Kasneci. 16th International Conference on Computer Analysis of Images and Patterns (CAIP 2015), 2015.
Driver-Activity Recognition in the Context of Conditionally Autonomous Driving
C. Braunagel, E. Kasneci, W. Stolzmann, and W. Rosenstiel. 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015), 2015.
Automated Comparison of Scanpaths in Dynamic Scenes
T. C. Kübler and E. Kasneci. Pfeiffer, Thies ; Essig, Kai (Hrsg.): Proceedings of the 2nd International Workshop on Solutions for Automatic Gaze Data Analysis 2015 (SAGA 2015), 2015.
Automated Visual Scanpath Analysis Reveals the Expertise Level of Micro-neurosurgeons
T. C. Kübler, S. Eivazi, and E. Kasneci. MICCAI 15 Workshop on Interventional Microscopy, 2015.
Driving with Glaucoma: Task Performance and Gaze Movements
T. C. Kübler, E. Kasneci, K. Aehling, M. Heister, W. Rosenstiel, U. Schiefer, and E. Papageorgiou. Optometry and Vision Science 92(11): 1037-1046, 2015.
Driving with Homonymous Visual Field Defects: Driving Performance and Compensatory Gaze Movements
T. C. Kübler, E. Kasneci, W. Rosenstiel, K. Aehling, M. Heister, K. Nagel, U. Schiefer, and E. Papageorgiou. Journal of Eye Movement Research 8(5): 1-11, 2015.
Arbitrarily shaped areas of interest based on gaze density gradient
W. Fuhl, T. C. Kübler, K. Sippel, W. Rosenstiel, and E. Kasneci. European Conference on Eye Movements, ECEM 2015, 2015.
2014
Driving with Binocular Visual Field Loss? A Study on a Supervised On-road Parcours with Simultaneous Eye and Head Tracking
E. Kasneci, K. Sippel, K. Aehling, M. Heister, W. Rosenstiel, U. Schiefer, and E. Papageorgiou. PLoS ONE 9(2): e87470, 2014.
The Applicability of Probabilistic Methods to the Online Recognition of Fixations and Saccades in Dynamic Scenes
E. Kasneci, G. Kasneci, T. C. Kübler, and W. Rosenstiel. Proceedings of the 8th Symposium on Eye Tracking Research and Applications, ETRA 2014, pages 323-326, 2014.
SubsMatch: Scanpath Similarity in Dynamic Scenes based on Subsequence Frequencies
T. C. Kübler, E. Kasneci, and W. Rosenstiel. Proceedings of the 8th Symposium on Eye Tracking Research and Applications, ETRA 2014, pages 319-322, 2014.
Rule-based classification of visual field defects
E. Kasneci, G. Kasneci, U. Schiefer, and W. Rosenstiel. 7th International Conference on Health Informatics (HEALTHINF 2014), 2014.
Stress-indicators and exploratory gaze for the analysis of hazard perception in patients with visual field loss
T. C. Kübler, E. Kasneci, W. Rosenstiel, U. Schiefer, K. Nagel, and E. Papageorgiou. Transportation Research Part F: Traffic Psychology and Behaviour 24: 231 - 243, 2014.
Binocular Glaucomatous Visual Field Loss and Its Impact on Visual Exploration - A Supermarket Study
K. Sippel, E. Kasneci, K. Aehling, M. Heister, W. Rosenstiel, U. Schiefer, and E. Papageorgiou. PLoS ONE 9(8): e106089, 2014.
Homonymous Visual Field Loss and its Impact on Visual Exploration - A Supermarket Study
E. Kasneci, K. Sippel, K. Aehling, M. Heister, W. Rosenstiel, U. Schiefer, and E. Papageorgiou. Translational Vision Science & Technology In Press, 2014.
Towards automated comparison of eye-tracking recordings in dynamic scenes
T. C. Kübler, D. R., J. Ungewiss, A. Wörner, C. Rothe, U. Schiefer, W. Rosenstiel, and E. Kasneci. EUVIP 2014, 2014.
2013
A New Method for Assessing the Exploratory Field of View (EFOV)
E. Tafaj, S. Hempel, M. Heister, K. Aehling, J. Dietzsch, F. Schaeffel, W. Rosenstiel, and U. Schiefer. 6th International Conference on Health Informatics, HEALTHINF 2013, 2013.
Online Classification of Eye Tracking Data for Automated Analysis of Traffic Hazard Perception
E. Tafaj, T. C. Kübler, G. Kasneci, W. Rosenstiel, and M. Bogdan. Artificial Neural Networks and Machine Learning (ICANN 2013), 2013.
Auswirkungen des visuellen Explorationsverhaltens von Patienten mit binokularen Gesichtsfelddefekten auf alltagsrelevante Tätigkeiten - Ergebnisse der TUTOR-Studie
U. Schiefer, T. C. Kübler, M. Heister, K. Aehling, K. Sippel, E. Papageorgiou, W. Rosenstiel, and E. Tafaj. 111. DOG-Kongress, 2013.
Towards the Automated Recognition of Assistance Need for Drivers with Impaired Visual Field
E. Kasneci. PhD thesis. Universität Tübingen, 2013.
2012
Erste Ergebnisse der TUTOR-Pilotstudie: Binokulare Gesichtsfeldausfälle und deren Auswirkungen auf die visuelle Exploration
U. Schiefer, K. Sippel, M. Heister, K. Aehling, C. Heine, K. Januschowski, E. Papageorgiou, W. Rosenstiel, and E. Tafaj. Ophthalmologische Nachrichten, 09.2012, 2012.
Bayesian Online Clustering of Eye-Tracking Data
E. Tafaj, G. Kasneci, W. Rosenstiel, and M. Bogdan. Eye Tracking Research and Applications (ETRA 2012), 2012.
2011
Reliable Classification of visual field defects in automated perimetry using clustering
E. Tafaj, J. Dietzsch, U. Schiefer, W. Rosenstiel, and M. Bogdan. Proceedings 8th IASTED/IEEE International Conference on Biomedical Engineering, pages 446 - 451, 2011.
Methode zur Messung der physiologischen Blendung im Fahrsimulator
V. Melcher, M. Aust, O. Stefani, E. Lösch, H. Wilhelm, and E. Tafaj. 57. Frühjahrskongress der Gesellschaft für Arbeitswissenschaft e. V.: "Mensch, Technik, Organisation - Vernetzung im Produktentstehungs- und -herstellungsprozess", Chemnitz, 03/2011, 2011.
Fast extraction of neuron morphologies from large-scale electron-microscopic image stacks
S. Lang, P. Drouvelis, E. Tafaj, P. Bastian, and B. Sakmann. Journal of Computational Neuroscience, 2011.
Zukünftige Fahrzeuge adaptieren sich auf den Fahrer: Identifikation charakteristischer Verhaltensmerkmale von Fahrzeugführern unter demographischen Gesichtspunkten
E. Tafaj, P. Rumbolz, M. Bogdan, J. Wiedemann, and W. Rosenstiel. GMM-Fachbericht Band 69 zur Tagung Automotive meets Electronics 2011, Dortmund, 2011.
Vishnoo - An Open-Source Software for Vision Research
E. Tafaj, T. C. Kübler, J. Peter, U. Schiefer, and M. Bogdan. 24th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2011), Bristol, UK, 2011.
2010
Introduction of a Portable Campimeter Based on a Laptop/Tablet PC
E. Tafaj, C. Uebber, J. Dietzsch, U. Schiefer, M. Bogdan, and W. Rosenstiel. Proceedings of the 19th Imaging and Perimetry Society (IPS), Tenerife, Spain, 2010.
Fahrzeugentwicklung für eine Gesellschaft im demografischen Wandel. H. Häfner, K. Beyreuther, W. Schlicht (Hrsg.). Altern gestalten, Medizin, Technik, Umwelt
J. Wiedemann, M. Horn, W. Rosenstiel, and E. Tafaj. , pages 109-120. Springer-Verlag Berlin Heidelberg 2010, 2010.
Teaching
Research
500,000 images closer to eyelid and pupil segmentation
We propose a fully convolutional neural networkfor pupil and eyelid segmentation as well as eyelid landmark and pupil ellipsis regression. The network is jointly trained using the Log loss forsegmentation and L1 loss for landmark and ellipsis regression. The ap-plication of the proposed network is the offline processing and creationof datasets. Which can be used to train resource-saving and real-timemachine learning algorithms such as random forests. In addition, we willprovide the worlds largest eye images dataset with more than 500,000images.
The applicability of Cycle GANs for pupil and eyelid segmentation, datageneration and image refinement
We evaluated Generative Adversarial Networks(GAN) for eyelid and pupil area segmentation, data gener-ation, and image refinement. While the segmentation GANperforms the desired task, the others serve as supportiveNetworks. The trained data generation GAN does not re-quire simulated data to increase the dataset, it simply usesexisting data and creates subsets. The purpose of the re-finement GAN, in contrast, is to simplify manual annota-tion by removing noise and occlusion in an image withoutchanging the eye structure and pupil position. In addition100,000 pupil and eyelid segmentations are made publiclyavailable for images from the labeled pupils in the wild dataset.
Neural networks for optical vector and eye ball parameter estimation
In this work we evaluate neural networks, support vector machinesand decision trees for the regression of the center of the eyeballand the optical vector based on the pupil ellipse. In the evaluationwe analyze single ellipses as well as window-based approaches asinput. Comparisons are made regarding accuracy and runtime. Theevaluation gives an overview of the general expected accuracy withdifferent models and amounts of input ellipses. A simulator wasimplemented for the generation of the training and evaluation data.For a visual evaluation and to push the state of the art in opticalvector estimation, the best model was applied to real data. Thisreal data came from public data sets in which the ellipse is alreadyannotated by an algorithm. The optical vectors on real data and thegenerator are made publicly available.
Blink Detection
A blink detection algorithm on eye images tailored towards head-mounted eye-trackers.
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.
Eye Movements Identification
Approaches for segmentation and synthesis of eye-tracking data using different neural networks and machine learning approaches.
EyeRecToo
In this paper, we introduce EyeRecToo, an open-source software for real-time pervasive head-mounted eye-tracking. Out of the box, EyeRecToo offers multiple real-time state-of-the-art pupil detection and gaze estimation methods, which can be easily replaced by user implemented algorithms if desired. A novel calibration method that allows users to calibrate the system without the assistance of a human supervisor is also integrated.
Eyetrace
Eyetrace is a tool for analysis of eye-tracking data. It has the approach to bunch a variety of different evaluation methods for a large share of eye trackers supporting scientific work and medical diagnosis. To allow EyeTrace to be compatible to different eye trackers, an additional tool called Eyetrace Butler is used. The Eyetrace Butler performs a data preprocessing and conversion for analysis with Eyetrace. It provides plugins for different eye trackers and converts their data into a format that can be imported and used by Eyetrace.
Intelligent Surgical Microscope
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 considered, e.g., closed source, costly eye tracker hardware dependencies, and requiring a human supervisor for calibration. In this paper, we introduce EyeRecToo, an open-source software for real-time pervasive head-mounted eye-tracking. Out of the box, EyeRecToo offers multiple real-time state-of-the-art pupil detection and gaze estimation methods, which can be easily replaced by user implemented algorithms if desired. A novel calibration method that allows users to calibrate the system without the assistance of a human supervisor is also integrated. Moreover, this software supports multiple head-mounted eye-tracking hardware, records eye and scene videos, and stores pupil and gaze information, which are also available as a real-time stream. Thus, EyeRecToo serves as a framework to quickly enable pervasive eye-tracking research and applications.
Robust Pupil Detection and Gaze Estimation
The reliable estimation of the pupil position in eye images is perhaps the most important prerequisite in gaze-based HMI applications. While there are many approaches that enable accurate pupil tracking under laboratory conditions, tracking the pupil in real-world images is highly challenging due to changes in illumination, reflections on glasses or on the eyeball, off-axis camera position, contact lenses, and many more.
Scanpath Comparison
Our eye movements are driven by a continuous trade-off between the need for detailed examination of objects of interest and the necessity to keep an overview of our surrounding. In consequence, behavioral patterns that are characteristic for our actions and their planning are typically manifested in the way we move our eyes to interact with our environment. Identifying such patterns from individual eye movement measurements is however highly challenging.
Vishnoo - A Visual Search Examination Tool
Vishnoo (Visual Search Examination Tool) is an integrated framework that combines configurable search tasks with gaze tracking capabilities, thus enabling the analysis of both, the visual field and the visual attention.