Seven papers accepted at ETRA 2018

The following papers were accepted at the 11th ACM International Symposium on Eye Tracking Research and Applications (ETRA 2018):

1. 

   Error-Aware Gaze-Based Interfaces for Robust Mobile Gaze Interaction

   Michael Barz, Florian Daiber, Daniel Sonntag, Andreas Bulling

   Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA), pp. 1–10, 2018.

   Abstract Links BibTeX Project Best paper award

   Gaze estimation error is unavoidable in head-mounted eye trackers and can severely hamper usability and performance of mobile gaze-based interfaces given that the error varies constantly for different interaction positions. In this work, we explore error-aware gaze-based interfaces that estimate and adapt to gaze estimation error on-the-fly. We implement a sample error-aware user interface for gaze-based selection and different error compensation methods: a naïve approach that increases component size directly proportional to the absolute error, a recent model by Feit et al. (CHI’17) that is based on the 2-dimensional error distribution, and a novel predictive model that shifts gaze by a directional error estimate. We evaluate these models in a 12-participant user study and show that our predictive model outperforms the others significantly in terms of selection rate, particularly for small gaze targets. These results underline both the feasibility and potential of next generation error-aware gaze-based user interfaces.

   doi: 10.1145/3204493.3204536

   Paper: barz18_etra.pdf

   @inproceedings{barz18_etra, author = {Barz, Michael and Daiber, Florian and Sonntag, Daniel and Bulling, Andreas}, title = {Error-Aware Gaze-Based Interfaces for Robust Mobile Gaze Interaction}, booktitle = {Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA)}, year = {2018}, pages = {1--10}, doi = {10.1145/3204493.3204536} }
2. 

   A novel approach to single camera, glint-free 3D eye model fitting including corneal refraction

   Kai Dierkes, Moritz Kassner, Andreas Bulling

   Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA), pp. 1–9, 2018.

   Abstract Links BibTeX Project

   Model-based methods for glint-free gaze estimation typically infer eye pose using pupil contours extracted from eye images. Existing methods, however, either ignore or require complex hardware setups to deal with refraction effects occurring at the corneal interfaces. In this work we provide a detailed analysis of the effects of refraction in glint-free gaze estimation using a single near-eye camera, based on the method presented by [Swirski et al. 2013]. We demonstrate systematic deviations in inferred eyeball positions and gaze directions with respect to synthetic ground-truth data and show that ignoring corneal refraction can result in angular errors of several degrees. Furthermore, we quantify gaze direction dependent errors in pupil radius estimates. We propose a novel approach to account for corneal refraction in 3D eye model fitting and by analyzing synthetic and real images show that our new method successfully captures refraction effects and helps to overcome the shortcomings of the state of the art approach.

   doi: 10.1145/3204493.3204525

   Paper: dierkes18_etra.pdf

   @inproceedings{dierkes18_etra, author = {Dierkes, Kai and Kassner, Moritz and Bulling, Andreas}, title = {A novel approach to single camera, glint-free 3D eye model fitting including corneal refraction}, booktitle = {Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA)}, year = {2018}, pages = {1--9}, doi = {10.1145/3204493.3204525} }
3. 

   Hidden Pursuits: Evaluating Gaze-selection via Pursuits when the Stimulus Trajectory is Partially Hidden

   Thomas Mattusch, Mahsa Mirzamohammad, Mohamed Khamis, Andreas Bulling, Florian Alt

   Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA), pp. 1–5, 2018.

   Abstract Links BibTeX Project

   The idea behind gaze interaction using Pursuits is to leverage the human’s smooth pursuit eye movements performed when following moving targets. However, humans can also anticipate where a moving target would reappear if it temporarily hides from their view. In this work, we investigate how well users can select targets using Pursuits in cases where the target’s trajectory is partially invisible (HiddenPursuits): e.g., can users select a moving target that temporarily hides behind another object? Although HiddenPursuits was not studied in the context of interaction before, understanding how well users can perform HiddenPursuits presents numerous opportunities, particularly for small interfaces where a target’s trajectory can cover area outside of the screen. We found that users can still select targets quickly via Pursuits even if their trajectory is up to 50% hidden, and at the expense of longer selection times when the hidden portion is larger. We discuss how gaze-based interfaces can leverage HiddenPursuits for an improved user experience.

   doi: 10.1145/3204493.3204569

   Paper: mattusch18_etra.pdf

   @inproceedings{mattusch18_etra, author = {Mattusch, Thomas and Mirzamohammad, Mahsa and Khamis, Mohamed and Bulling, Andreas and Alt, Florian}, title = {Hidden Pursuits: Evaluating Gaze-selection via Pursuits when the Stimulus Trajectory is Partially Hidden}, booktitle = {Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA)}, year = {2018}, pages = {1--5}, doi = {10.1145/3204493.3204569} }
4. 

   Robust Eye Contact Detection in Natural Multi-Person Interactions Using Gaze and Speaking Behaviour

   Philipp Müller, Michael Xuelin Huang, Xucong Zhang, Andreas Bulling

   Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA), pp. 1–10, 2018.

   Abstract Links BibTeX Project

   Eye contact is one of the most important non-verbal social cues and fundamental to human interactions. However, detecting eye contact without specialized eye tracking equipment poses significant challenges, particularly for multiple people in real-world settings. We present a novel method to robustly detect eye contact in natural three- and four-person interactions using off-the-shelf ambient cameras. Our method exploits that, during conversations, people tend to look at the person who is currently speaking. Harnessing the correlation between people’s gaze and speaking behaviour therefore allows our method to automatically acquire training data during deployment and adaptively train eye contact detectors for each target user. We empirically evaluate the performance of our method on a recent dataset of natural group interactions and demonstrate that it achieves a relative improvement over the state-of-the-art method of more than 60%, and also improves over a head pose based baseline.

   doi: 10.1145/3204493.3204549

   Paper: mueller18_etra.pdf

   @inproceedings{mueller18_etra, author = {M{\"{u}}ller, Philipp and Huang, Michael Xuelin and Zhang, Xucong and Bulling, Andreas}, title = {Robust Eye Contact Detection in Natural Multi-Person Interactions Using Gaze and Speaking Behaviour}, booktitle = {Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA)}, year = {2018}, pages = {1--10}, doi = {10.1145/3204493.3204549} }
5. 

   Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings

   Seonwook Park, Xucong Zhang, Andreas Bulling, Otmar Hilliges

   Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA), pp. 1–10, 2018.

   Abstract Links BibTeX Project Best presentation award

   Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras. In unconstrained real-world settings, however, such methods are surpassed by recent appearance-based methods due to difficulties in modeling factors such as illumination changes and other visual artifacts. We present a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods. Despite having been trained exclusively on synthetic data, our method exceeds the state of the art for iris localization and eye shape registration on real-world imagery. We then use the detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods. Our approach outperforms existing model-fitting and appearance-based methods in the context of person-independent and personalized gaze estimation.

   doi: 10.1145/3204493.3204545

   Paper: park18_etra.pdf

   Video: https://www.youtube.com/watch?v=I8WlEHgDBV4

   @inproceedings{park18_etra, author = {Park, Seonwook and Zhang, Xucong and Bulling, Andreas and Hilliges, Otmar}, title = {Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings}, booktitle = {Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA)}, year = {2018}, pages = {1--10}, doi = {10.1145/3204493.3204545}, video = {https://www.youtube.com/watch?v=I8WlEHgDBV4} }
6. 

   Fixation Detection for Head-Mounted Eye Tracking Based on Visual Similarity of Gaze Targets

   Julian Steil, Michael Xuelin Huang, Andreas Bulling

   Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA), pp. 1–9, 2018.

   Abstract Links BibTeX Project

   Fixations are widely analysed in human vision, gaze-based interaction, and experimental psychology research. However, robust fixation detection in mobile settings is profoundly challenging given the prevalence of user and gaze target motion. These movements feign a shift in gaze estimates in the frame of reference defined by the eye tracker’s scene camera. To address this challenge, we present a novel fixation detection method for head-mounted eye trackers. Our method exploits that, independent of user or gaze target motion, target appearance remains about the same during a fixation. It extracts image information from small regions around the current gaze position and analyses the appearance similarity of these gaze patches across video frames to detect fixations. We evaluate our method using fine-grained fixation annotations on a five-participant indoor dataset (MPIIEgoFixation) with more than 2,300 fixations in total. Our method outperforms commonly used velocity- and dispersion-based algorithms, which highlights its significant potential to analyse scene image information for eye movement detection.

   doi: 10.1145/3204493.3204538

   Paper: steil18_etra.pdf

   @inproceedings{steil18_etra, author = {Steil, Julian and Huang, Michael Xuelin and Bulling, Andreas}, title = {Fixation Detection for Head-Mounted Eye Tracking Based on Visual Similarity of Gaze Targets}, booktitle = {Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA)}, year = {2018}, pages = {1--9}, doi = {10.1145/3204493.3204538} }
7. 

   Revisiting Data Normalization for Appearance-Based Gaze Estimation

   Xucong Zhang, Yusuke Sugano, Andreas Bulling

   Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA), pp. 1–9, 2018.

   Abstract Links BibTeX Project

   Appearance-based gaze estimation is promising for unconstrained real-world settings, but the significant variability in head pose and user-camera distance poses significant challenges for training generic gaze estimators. Data normalization was proposed to cancel out this geometric variability by mapping input images and gaze labels to a normalized space. Although used successfully in prior works, the role and importance of data normalization remains unclear. To fill this gap, we study data normalization for the first time using principled evaluations on both simulated and real data. We propose a modification to the current data normalization formulation by removing the scaling factor and show that our new formulation performs significantly better (between 9.5% and 32.7%) in the different evaluation settings. Using images synthesized from a 3D face model, we demonstrate the benefit of data normalization for the efficiency of the model training. Experiments on real-world images confirm the advantages of data normalization in terms of gaze estimation performance.

   doi: 10.1145/3204493.3204548

   Paper: zhang18_etra.pdf

   @inproceedings{zhang18_etra, author = {Zhang, Xucong and Sugano, Yusuke and Bulling, Andreas}, title = {Revisiting Data Normalization for Appearance-Based Gaze Estimation}, booktitle = {Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA)}, year = {2018}, pages = {1--9}, doi = {10.1145/3204493.3204548} }

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