Head Pose Estimation by a Stepwise Nonlinear Regression

Head pose estimation is a crucial step for numerous face applications such as gaze tracking and face recognition. In this paper, we introduce a new method to learn the mapping between a set of features and the corresponding head pose. It combines a. filter based feature selection and a Generalized Regression Neural Network where inputs arc: sequentially selected through a boosting process. We propose the Fuzzy Functional Criterion, a new filter used to select relevant features. At each step; features are evaluated using weights on examples computed using the error produced by the neural network at the previous step. This boosting strategy helps to focus on hard examples and selects a set of complementary features. Results are compared with three state-of-the-art methods on the Pointing 04 database.