A discriminative parts based model approach for fiducial points free and shape constrained head pose normalisation in the wild

Continuous Confidence Map Based Normalisation: While continuous head pose normalisation is not the goal of this paper, we demonstrate as a proof of concept that it is possible to extend the current method for continuous head pose normalisation. For dealing with faces in videos [1], continuous head pose normalisation is required. [2] argue that the appearance of a part does not changes with a subtle pose change, therefore a detector for part i in pose angle p can be shared for the same part i for a pose angle p + δ. Further experiments in [2] showed that sharing based models and independent model have comparable performance. However, sharing based models are faster upto ten times as compared to the independent models [2]. The confidence maps based methods (CM-HPN_PS and CM-HPN_PI) can be extended from discrete to continuous by sharing part-specific regression models R, which are shared among neighboring pose angles.