Estimating change in head pose from low resolution video using LBP-based tracking

Head pose estimation from low resolution video can be a difficult task due to the limited quantity and quality of pixels which can be harvested for information. In this work, we propose a method for tracking change in head pose by solving a texture detection problem in an orientation space. In another words, the current head pose can be tracked by employed a local binary pattern based comparison between its texture and several textures estimated from rotating the previous head pose by several known angles. Thus, the selected previous head pose can be regarded as an estimator. To boost the tracking accuracy, multiple estimators, each with different previous head pose, are deployed to produce a combined result. Empirically, the proposed method outperforms the state of the art methods for head pose tracking in low resolution image sequence. Furthermore, the method also performs well on image sequences with varying light.