High-precision Detection of Facial Landmarks to Estimate Head Motions Based on Vision Models

Abstract

A new approach of determination of head movement is presented from the pictures recorded via digital cameras monitoring the scanning processing of PET. Two human vision models of CIECAMs and BMV are applied to segment the face region via skin colour and to detect local facial landmarks respectively. The developed algorithms are evaluated on the pictures (n=12) monitoring a subject’s head while simulating PET scanning captured by two calibrated cameras (located in the front and left side from a subject). It is shown that centers of chosen facial landmarks of eye corners and middle point of nose basement have been detected with very high precision (1 ± 0.64 pixels). Three landmarks on pictures received by the front camera and two by the side camera have been identified. Preliminary results on 2D images with known moving parameters show that movement parameters of rotations and translations along X, Y, and Z directions can be obtained very accurately via the described methods.