Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis
This work addresses medical diagnosis using atypical facial features, but it appears incremental as it applies an existing method to a new medical dataset.
The study tackled facial landmark detection for atypical 3D facial modelling in facial palsy cases, finding that the stacked hourglass architecture outperformed traditional methods in accuracy on unseen atypical faces.
While facial biometrics has been widely used for identification purpose, it has recently been researched as medical biometrics for a range of diseases. In this chapter, we investigate the facial landmark detection for atypical 3D facial modelling in facial palsy cases, while potentially such modelling can assist the medical diagnosis using atypical facial features. In our work, a study of landmarks localisation methods such as stacked hourglass networks is conducted and evaluated to ascertain their accuracy when presented with unseen atypical faces. The evaluation highlights that the state-of-the-art stacked hourglass architecture outperforms other traditional methods.