  We present a fully automated system for sizing nasal Positive Airway Pressure
(PAP) masks. The system is comprised of a mix of HOG object detectors as well
as multiple convolutional neural network stages for facial landmark detection.
The models were trained using samples from the publicly available PUT and MUCT
datasets while transfer learning was also employed to improve the performance
of the models on facial photographs of actual PAP mask users. The fully
automated system demonstrated an overall accuracy of 64.71% in correctly
selecting the appropriate mask size and 86.1% accuracy sizing within 1 mask
size.
