Head pose estimation through multi-class face segmentation

Khalil Khan, Massimo Mauro, Pierangelo Migliorati, Riccardo Leonardi

Abstract: The aim of this work is to explore the usefulness of face semantic segmentation for head pose estimation. We implement a multi-class face segmentation algorithm and we train a model for each considered pose. Given a new test image, the probabilities associated to face parts by the different models are used as the only information for estimating the head orientation. A simple algorithm is proposed to exploit such probabilites in order to predict the pose. The proposed scheme achieves competitive results when compared to most recent methods, according to mean absolute error and accuracy metrics. Moreover, we release and make publicly available a face segmentation dataset 1 consisting of 294 images belonging to 13 different poses, manually labeled into six semantic regions, which we used to train the segmentation models.