A lightweight 3D dense facial landmark estimation model from position
  map data
Shubhajit Basak Sathish Mangapuram Gabriel Costache Rachel McDonnell Michael Schukat
The incorporation of 3D data in facial analysis tasks has gained popularity
in recent years. Though it provides a more accurate and detailed representation
of the human face, accruing 3D face data is more complex and expensive than 2D
face images. Either one has to rely on expensive 3D scanners or depth sensors
which are prone to noise. An alternative option is the reconstruction of 3D
faces from uncalibrated 2D images in an unsupervised way without any ground
truth 3D data. However, such approaches are computationally expensive and the
learned model size is not suitable for mobile or other edge device
applications. Predicting dense 3D landmarks over the whole face can overcome
this issue. As there is no public dataset available containing dense landmarks,
we propose a pipeline to create a dense keypoint training dataset containing
520 key points across the whole face from an existing facial position map data.
We train a lightweight MobileNet-based regressor model with the generated data.
As we do not have access to any evaluation dataset with dense landmarks in it
we evaluate our model against the 68 keypoint detection task. Experimental
results show that our trained model outperforms many of the existing methods in
spite of its lower model size and minimal computational cost. Also, the
qualitative evaluation shows the efficiency of our trained models in extreme
head pose angles as well as other facial variations and occlusions.