Head pose estimation by nonlinear manifold learning

In This work we propose an isomap-based nonlinear alternative to the linear subspace method for manifold representation of view-varying faces. Being interested in user-independent head pose estimation, we extend the isomap model (J.B. Tenenbaum et al., 2000) to be able to map (high-dimensional) input data points which are not in the training data set into the dimensionality-reduced space found by the model. From this representation, a pose parameter map relating the input face samples to view angles is learnt. The proposed method is evaluated on a large database of multi-view face images in comparison to two other recently proposed subspace methods.