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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 wellas multiple convolutional neural network stages for facial landmark detection.The models were trained using samples from the publicly available PUT and MUCTdatasets while transfer learning was also employed to improve the performanceof the models on facial photographs of actual PAP mask users. The fullyautomated system demonstrated an overall accuracy of 64.71% in correctlyselecting the appropriate mask size and 86.1% accuracy sizing within 1 masksize.

A Fully Automated System for Sizing Nasal PAP Masks Using Facial Photographs