EYEDIAP

The EYEDIAP dataset was designed to train and evaluate gaze estimation algorithms from RGB and RGB-D data. It contains a diversity of participants, head poses, gaze targets and sensing conditions.

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Database description | Session description | Evaluation | Publications | FAQs

The lack of a common benchmark for the evaluation of the gaze estimation task from RGB and RGB-D data is a serious limitation for distinguishing the advantages and disadvantages of the many proposed algorithms found in the literature.

The EYEDIAP dataset intends to fill the need for a standard database for gaze estimation from remote RGB, and RGB-D (standard vision and depth), cameras. The recording methodology was designed such that we systematically include, and isolate, most of the variables which affect the remote gaze estimation algorithms:

  • Head pose variations.
  • Person variation.
  • Changes in ambient and sensing condition.
  • Types of target: screen or 3D object.

Some pre-defined benchmarks are provided to evaluate each one of these aspects in an independent manner, and the data was preprocessed to extract and provide complementary observations (e.g. head pose).


 

   The recording setup is as shown in the image below:
setup.png

Set-up

The set-up is composed of the following elements:

  • Kinect: this consumer device provides standard (RGB) and Depth video streams at VGA resolution (640x480) and 30fps.
  • HD camera: the Kinect was designed with a large field of view imposing less restriction on user mobility but this is problematic for eye tracking based on VGA resolution. Therefore, we also recorded the scene with a full HD camera (1920x1080) at 25fps. The provided videos are synchronized with the Kinect data at 30fps.
  • LEDs: 5 LEDs visible to both cameras were used to synchronize the RGB-D and HD streams.
  • Flat screen: we used a 24" screen to display a visual target.
  • Small ball: we used a 4cm diameter ball as a visual target with a double purpose: to serve as a visual target in a 3D environment and be discriminative in both RGB and depth data such that its 3D position could be precisely tracked
This set-up was used for the recording of 94 sessions, each with different characteristics.

Data per session

At each session folder, the following files can be found:

  • rgb_vga.mov : the RGB Kinect video. Encoded using MPEG-4.
  • depth.mov : the Depth video. Encoded using ZLIB.
  • rgb_hd.mov: the RGB HD video. Encoded using MPEG-4 (The HD video is not available for a few sessions).
  • head_pose.txt : the frame-by-frame head pose parameters.
  • eye_tracking.txt : the frame-by-frame 2D and 3D eyes position.
  • ball_tracking.txt : the frame-by-frame 2D and 3D position of the ball target (if relevant).
  • screen_coordinates.txt : the frame-by-frame 2D and 3D screen coordinates (if relevant).
  • rgb_vga_calibration.txt: the calibration parameters for the RGB Kinect camera.
  • depth_calibration.txt: the calibration parameters for the Depth camera.
  • rgb_hd_calibration.txt: the calibration parameters for the RGB HD camera.

In total, the EYEDIAP database is composed of 94 sessions. For a list of the sessions, go here.

   1_A_FT_M_depth_443.png

1_A_FT_M_rgb_hd_443.png

1_A_FT_M_rgb_vga_443.png

 

Documentation

For more details on the processing methodology and data interpretation, please refer to this document:

Funes Mora, K. A., Monay, F., and Odobez, J.-M. 2014. EYEDIAP database: Data description and gaze tracking evaluation benchmarks. Tech. Rep. RR-08-2014, Idiap, May 2014.

which you can find > here <.

Each recording session is of a combination of the following parameters:

  • Participants. We have recorded 16 people: 12 male and 4 female.
  • Recording conditions. For participant 14, 15 and 16, some sessions were recorded twice, in two different conditions (denoted A or B): different day, illumination and distance to the camera.
  • Visual Target. It is the object which the participant was requested to gaze at. To be representative of different applications, we included the following cases: Discrete screen target (DS), where a small circle was uniformly drawn every 1.1 seconds on random locations in the computer screen; Continuous screen target (CS), in which the circle was programmed to move along a random trajectory for 2s, to obtain examples with smoother gaze movement; 3D floating target (FT): a ball with a 4cm diameter hanging from a thin thread attached to a stick that was moved within a 3D region between the camera and the participant. In contrast to the screen target, the participant was at a larger distance (1.2m instead of 80-90cm) from the camera to allow sufficient space for the target to move.
  • Head pose. To evaluate methods in terms of robustness to head pose, we asked participants to keep gazing at the visual target while (i) keeping an approximately static head pose facing towards the screen (Static case, S); or (ii) performing head movements (translation and rotation) to introduce head pose variations (Mobile case, M).

Visualization

To inspect the different sessions please run the following command within the "Scripts" folder:

python view_session.py <session_idx>

Where session_idx is the session index ( [0, 93] ). By browsing through the code the user can find how to interpret the per session meta-data.

Sessions list

Each session is denoted by the string "P-C-T-H" which refers to the participant P=(1-16), the recording conditions C=(A or B), the target T=(DS, CS or FT) and the head pose H=(S or M) respectively. The 94 provided sessions are the following:

 

Session index Participant Conditions Visual target Head pose
0 1 A DS S
1 1 A DS M
2 1 A CS S
3 1 A CS M
4 1 A FT S
5 1 A FT M
6 2 A DS S
7 2 A DS M
8 2 A CS S
9 2 A CS M
10 2 A FT S
11 2 A FT M
12 3 A DS S
13 3 A DS M
14 3 A CS S
15 3 A CS M
16 3 A FT S
17 3 A FT M
18 4 A DS S
19 4 A DS M
20 4 A CS S
21 4 A CS M
22 4 A FT S
23 4 A FT M
24 5 A DS S
25 5 A DS M
26 5 A CS S
27 5 A CS M
28 5 A FT S
29 5 A FT M
30 6 A DS S
31 6 A DS M
32 6 A CS S
33 6 A CS M
34 6 A FT S
35 6 A FT M
36 7 A DS S
37 7 A DS M
38 7 A CS S
39 7 A CS M
40 7 A FT S
41 7 A FT M
42 8 A DS S
43 8 A DS M
44 8 A CS S
45 8 A CS M
46 8 A FT S
47 8 A FT M
48 9 A DS S
49 9 A DS M
50 9 A CS S
51 9 A CS M
52 9 A FT S
53 9 A FT M
54 10 A DS S
55 10 A DS M
56 10 A CS S
57 10 A CS M
58 10 A FT S
59 10 A FT M
60 11 A DS S
61 11 A DS M
62 11 A CS S
63 11 A CS M
64 11 A FT S
65 11 A FT M
66 12 B FT S
67 12 B FT M
68 13 B FT S
69 13 B FT M
70 14 A DS S
71 14 A DS M
72 14 A CS S
73 14 A CS M
74 14 A FT S
75 14 A FT M
76 14 B FT S
77 14 B FT M
78 15 A DS S
79 15 A DS M
80 15 A CS S
81 15 A CS M
82 15 A FT S
83 15 A FT M
84 15 B FT S
85 15 B FT M
86 16 A DS S
87 16 A DS M
88 16 A CS S
89 16 A CS M
90 16 A FT S
91 16 A FT M
92 16 B FT S
93 16 B FT M

If you have used this database and you would like your results to appear here, please send an email to kenneth.funes@idiap.ch

EYEDIAP experiment

The gaze tracking results reported in our ETRA 2014 paper are provided within the "Example" folder.

To recompute the reported metrics, such as the gaze estimation accuracy, please run the following command:

python compute_etra_results.py

within the "Scripts" folder.

Reference

If you use this dataset, please cite the following paper:

Kenneth Alberto Funes Mora, Florent Monay and Jean-Marc Odobez, “EYEDIAP, A Database for the Development and Evaluation of Gaze Estimation Algorithms from RGB and RGB-D Cameras”, in ACM Symposium on Eye Tracking Research and Applications , 2014.
10.1145/2578153.2578190

Please also refer to our technical report for a extended description of the dataset, which you can find > here <.

FAQ

Is there something wrong with the 3D coordinates given in the screen_coordinates.txt file?

The 2D coordinates are perfectly fine. However, the given screen 3D coordinates are actually referred to the RGB camera coordinate system. To refer them to the World Coordinate System (WCS), you'd have to apply the transformation described in Equation 1 from this document.

Is there some sample code available?

Within the database distribution we provide a couple of sample scripts to visualise the given data.

What about some sample code for 3D processing?

Yes! Have a look at the RGBD python processing module. It is fully compatible with the EYEDIAP database and it should be easy to start 3D rendering the database.

Is there detailed documentation available?

Definitely. Have a look at the >tech report<