A framework for head pose estimation and face segmentation through conditional random fields

This paper explores the usefulness of conditional random fields through the idea of semantic face segmentation in the challenging task of head pose estimation. A multi-class face segmentation algorithm based on conditional random fields is implemented to develop a model for each discrete pose. When a new test image is given as input to the face segmentation framework, the trained model predicts probabilities for each face part. These probabilities are then used for estimation of head pose. The proposed framework is evaluated on four standard databases, namely Pointing’04, AFLW, BU and ICT-3DHPE. Two standard metrics, mean absolute error and pose estimation accuracy are used for evaluation of the head pose estimation part. Pixel labeling accuracy is used to assess the segmentation results. The experimental results show that better results are obtained as compared to state-of-the-art.