Probabilistic principal component analysis for texture modelling of adaptive active appearance models and its application for head pose estimation
This study suggests an application of human–robot interaction based on three-dimensional real-time monocular head pose tracker in which active appearance models (AAMs) are utilised to extract facial features. In order to improve texture model, two probabilistic approaches are proposed for principal component analysis in the presence of missing values. It is observed that using the suggested Bayesian model not only increases the fitting accuracy of the model, but also reduces model parameters which may cause an increase in the speed of model fitting. Moreover, contrary to the common assumption in AAM, the gradient matrix must not be supposed to be constant. In this investigation, a method is suggested in which the gradient matrix is adapted with new images during model fitting of video sequences as much as possible. In the next step, by means of suggested methods, operator's head pose will be estimated by POSIT algorithm and by its implementation on PeopleBot robot, enhancement of the interaction between human and robot is presented in order to control the orientation of the robot camera.