Pose estimation using facial feature points and manifold learning

This paper presents robust facial pose estimation techniques based on the underlying low dimensional manifolds embedded in facial images of varying pose. In our approach, facial feature points of training faces are converted to a low dimensional projection space to form a smooth manifold surface. Subsequent faces are automatically detected, facial feature points are extracted and mapped onto the low dimensional projection surface, where regression models robustly estimate pose. The benefit of using facial feature points for manifold learning over raw facial images is demonstrated by a variety of experiments. Linear, nonlinear, unsupervised and supervised methods are considered including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Locally Linear Embedding (LLE), Isomap, unsupervised Locality Preserving Projections (LPP), and supervised LPP (SLPP).