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The datasets used for training the new gaze tracker are now mentioned in the README file.

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Emiliana 2020-02-24 23:31:35 +01:00
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README.md
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@ -62,10 +62,9 @@ Up to 25 expressions are supported, but a more reasonable number is 5-6.
# General notes
* The tracking seems to be quite robust even with partial occlusion of the face, glasses or bad lighting conditions.
* To detect whether eyes are opened or closed, a threshold should be applied to the `rightEyeOpen`/`leftEyeOpen` fields. The landmark points are not very sensitive to this.
* While there is a gaze tracking model, its results are very noisy, so they are not yet accessible through the `OpenSee` component.
* There is now an experimental gaze tracking model.
* Depending on the frame rate, face tracking can easily use up a whole CPU core. At 30fps for a single face, it should still use less than 100% of one core on a decent CPU.
* When setting the number of faces to track to a higher number than the number of faces actually in view, the OpenCV face detecter will attempt to find new faces every `--scan-every` frames. It can be quite slow, so try to set `--faces` no higher than the actual number of faces you are tracking.
* When setting the number of faces to track to a higher number than the number of faces actually in view, the OpenCV face detection will attempt to find new faces every `--scan-every` frames. It can be quite slow, so try to set `--faces` no higher than the actual number of faces you are tracking.
# Models
@ -121,7 +120,7 @@ Additional training has been done on the WFLW dataset after reducing it to 66 po
year = {2018}
}
For the training the gaze and blink detection model, the WFLW dataset and [MRL Eye](http://mrl.cs.vsb.cz/eyedataset) datasets were used.
For the training the gaze and blink detection model, the [MPIIGaze](https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild/) dataset was used. Additionally, around 125000 synthetic eyes generated with [UnityEyes](https://www.cl.cam.ac.uk/research/rainbow/projects/unityeyes/) were used during training.
## Algorithm