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2D Polygon
Eye
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License: Custom

Overview

This is a novel dataset of 66 high-quality, high-speed eye region videos for the development
and evaluation of pupil detection algorithms. The videos in our dataset were recorded from
22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted
eye tracker. They cover people with different ethnicities, a diverse set of everyday indoor
and outdoor illumination environments, as well as natural gaze direction distributions. The
dataset also includes participants wearing glasses, contact lenses, as well as make-up. We
benchmark five state-of-the-art pupil detection algorithms on our dataset with respect to robustness
and accuracy. We further study the influence of image resolution, vision aids, as well as recording
location (indoor, outdoor) on pupil detection performance. Our evaluations provide valuable
insights into the general pupil detection problem and allow us to identify key challenges for
robust pupil detection on head-mounted eye trackers.

Data Collection

Participants

Detailed information about our participants can be found in Table 2. We recruited 22 participants
including 9 female through university mailing lists and personal communication. Among them
are five different ethnicities: 11 Indian, 6 German, 2 Pakistani, 2 Iranian, and 1 Egyptian.
In total we had five different eye colors: 12 brown, 5 black, 3 blue-gray, 1 blue-green, 1
green. Also 5 people had impaired vision, 2 wore glasses and 1 wore contact lenses. Strong
eye make-up was worn by 1 person (with participant ID 22).

Apparatus

The eye tracker used for the recording
was a high-speed Pupil Pro head-mounted eye tracker that record eye videos with 120 Hz [Kass-
ner et al. 2014]. In order to capture high frame rate scene videos, we replaced the original
scene camera with a PointGrey Chameleon3 USB3.0 camera recording at up to 149 Hz. The hardware
set up is shown in Figure 2a and Figure 2b. It allowed us to record all videos with 95 FPS,
which is a speed at which even fast eye movements last through several frames.

Procedure

As shown in
the right image below, the participants were instructed to look at a moving red ball as a fixation
target during the data collection. The position of the red ball in the visual field of the
participant is shown in middle image below with an image captured by the scene camera. In order
to cover as many different conditions as possible, we randomly picked the recording locations
in and around of several buildings. Each location was not chosen more than once during the
whole recording of all participants. 34.3% of the recordings were done outdoors, in 84.7% natural
light was present and in 33.6% artificial light was present. Besides locations, we have also
tweaked the angle of the eye cameras such that the dataset contains a wide range of camera
angles from frontal views to highly off-axis angles. This is done by either asking the participant
to take the tracker off and put it back on, or manually moving the camera. With each of the
22 participant we recorded three videos with around 20 seconds length, yielding 130,856 images
overall.Participants could keep their glasses and contact lenses on during the recording.

Data Annotation

We used different methods for annotation. In many easy cases such as some indoor recordings,
the pupil area has a clear boundary and no strong reflections inside. We annotated these frames
by manually selecting 1 or 2 points inside the pupil area, using them as seed points to find
the largest connected area with similar intensity values. The pupil center is defined as the
centroid of this area. Some recordings have a clear scene video but strong reflections/noise
in the eye video, such as outdoor recordings under strong sunlight. In those cases, we tracked
the fixation target (red ball) in the scene videos and manually annotated part of the eye pupil
positions in the eye videos. From this calibration data we com- puted a mapping function from
target positions to pupil positions. In addition, we examined the annotated videos again to
find wrong annotations, and corrected them by selecting 5 or more points on the pupil boundary
and fitting an ellipse to them. The center of the ellipse was used as a refined pupil center
position.

Citation

@inproceedings{tonsen2016labelled,
  title={Labelled pupils in the wild: a dataset for studying pupil detection in unconstrained
environments},
  author={Tonsen, Marc and Zhang, Xucong and Sugano, Yusuke and Bulling, Andreas},
  booktitle={Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking
Research \& Applications},
  pages={139--142},
  year={2016}
}

License

Custom

Data Summary
Type
Video,
Amount
--
Size
2.4GB
Provided by
Max Planck Institute for Informatics, Saarbrucken, Germany
| Amount -- | Size 2.4GB
LPW
2D Polygon
Eye
License: Custom

Overview

This is a novel dataset of 66 high-quality, high-speed eye region videos for the development
and evaluation of pupil detection algorithms. The videos in our dataset were recorded from
22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted
eye tracker. They cover people with different ethnicities, a diverse set of everyday indoor
and outdoor illumination environments, as well as natural gaze direction distributions. The
dataset also includes participants wearing glasses, contact lenses, as well as make-up. We
benchmark five state-of-the-art pupil detection algorithms on our dataset with respect to robustness
and accuracy. We further study the influence of image resolution, vision aids, as well as recording
location (indoor, outdoor) on pupil detection performance. Our evaluations provide valuable
insights into the general pupil detection problem and allow us to identify key challenges for
robust pupil detection on head-mounted eye trackers.

Data Collection

Participants

Detailed information about our participants can be found in Table 2. We recruited 22 participants
including 9 female through university mailing lists and personal communication. Among them
are five different ethnicities: 11 Indian, 6 German, 2 Pakistani, 2 Iranian, and 1 Egyptian.
In total we had five different eye colors: 12 brown, 5 black, 3 blue-gray, 1 blue-green, 1
green. Also 5 people had impaired vision, 2 wore glasses and 1 wore contact lenses. Strong
eye make-up was worn by 1 person (with participant ID 22).

Apparatus

The eye tracker used for the recording
was a high-speed Pupil Pro head-mounted eye tracker that record eye videos with 120 Hz [Kass-
ner et al. 2014]. In order to capture high frame rate scene videos, we replaced the original
scene camera with a PointGrey Chameleon3 USB3.0 camera recording at up to 149 Hz. The hardware
set up is shown in Figure 2a and Figure 2b. It allowed us to record all videos with 95 FPS,
which is a speed at which even fast eye movements last through several frames.

Procedure

As shown in
the right image below, the participants were instructed to look at a moving red ball as a fixation
target during the data collection. The position of the red ball in the visual field of the
participant is shown in middle image below with an image captured by the scene camera. In order
to cover as many different conditions as possible, we randomly picked the recording locations
in and around of several buildings. Each location was not chosen more than once during the
whole recording of all participants. 34.3% of the recordings were done outdoors, in 84.7% natural
light was present and in 33.6% artificial light was present. Besides locations, we have also
tweaked the angle of the eye cameras such that the dataset contains a wide range of camera
angles from frontal views to highly off-axis angles. This is done by either asking the participant
to take the tracker off and put it back on, or manually moving the camera. With each of the
22 participant we recorded three videos with around 20 seconds length, yielding 130,856 images
overall.Participants could keep their glasses and contact lenses on during the recording.

Data Annotation

We used different methods for annotation. In many easy cases such as some indoor recordings,
the pupil area has a clear boundary and no strong reflections inside. We annotated these frames
by manually selecting 1 or 2 points inside the pupil area, using them as seed points to find
the largest connected area with similar intensity values. The pupil center is defined as the
centroid of this area. Some recordings have a clear scene video but strong reflections/noise
in the eye video, such as outdoor recordings under strong sunlight. In those cases, we tracked
the fixation target (red ball) in the scene videos and manually annotated part of the eye pupil
positions in the eye videos. From this calibration data we com- puted a mapping function from
target positions to pupil positions. In addition, we examined the annotated videos again to
find wrong annotations, and corrected them by selecting 5 or more points on the pupil boundary
and fitting an ellipse to them. The center of the ellipse was used as a refined pupil center
position.

Citation

@inproceedings{tonsen2016labelled,
  title={Labelled pupils in the wild: a dataset for studying pupil detection in unconstrained
environments},
  author={Tonsen, Marc and Zhang, Xucong and Sugano, Yusuke and Bulling, Andreas},
  booktitle={Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking
Research \& Applications},
  pages={139--142},
  year={2016}
}

License

Custom

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