  Advances in Deep Learning have made possible reliable landmark tracking of
human bodies and faces that can be used for a variety of tasks. We test a
recent Computer Vision solution, MediaPipe Holistic (MPH), to find out if its
tracking of the facial features is reliable enough for a linguistic analysis of
data from sign languages, and compare it to an older solution (OpenFace, OF).
We use an existing data set of sentences in Kazakh-Russian Sign Language and a
newly created small data set of videos with head tilts and eyebrow movements.
We find that MPH does not perform well enough for linguistic analysis of
eyebrow movement - but in a different way from OF, which is also performing
poorly without correction. We reiterate a previous proposal to train additional
correction models to overcome these limitations.
