Robust Head Pose Estimation Using Supervised Manifold Learning

Abstract

We address the problem of fine-grain head pose angle estimation from a single 2D face image as a continuous regression problem. Currently the state of the art, and a promising line of research, on head pose estimation seems to be that of nonlinear manifold embedding techniques, which learn an "optimal" low-dimensional manifold that models the nonlinear and continuous variation of face appearance with pose angle. Furthermore, supervised manifold learning techniques attempt to achieve this robustly in the presence of latent variables in the training set (especially identity, illumination, and facial expression), by incorporating head pose angle information accompanying the training samples. Most of these techniques are designed with the classification scenario in mind, however, and are not directly applicable to the regression scenario where continuous numeric values (pose angles), rather than class labels (discrete poses), are available. In this paper, we propose to deal with the regression case in a principled way. We present a taxonomy of methods for incorporating continuous pose angle information into one or more stages of the manifold learning process, and discuss its implementation for Neighborhood Preserving Embedding (NPE) and Locality Preserving Projection (LPP). Experiments are carried out on a face dataset containing significant identity and illumination variations, and the results show that our regression-based approach far outperforms previous supervised manifold learning methods for head pose estimation.

Head Pose Estimation via Manifold Learning

“…Experimental results obtained by the proposed method performing on the database of FacePix [25] and MIT-CBCL [31] show big improvements compared with other state-of-the-art algorithms [23,26] in Stage 3 and Stages 1+2+3 .This means that this method is more robust for identity and illumination variations.…”

“…A taxonomy of methods, which structures the general framework of manifold learning into several stages, is proposed to incorporate the head pose angles in one or some of the stages to enable the supervised manifold learning [26]. A straightforward solution could be the adaption of the distance and weight matrix according to the angle difference between pairwise face images.…”

Automatic head pose estimation with Synchronized sub manifold embedding and Random Regression Forests

“…In contrast Ben Abdelkader [16] shows a direct way to incorporate label distance related information into the objective functions of LLE and LE. Wang, et al, [17] performs two steps of dimensionality reduction, first an unsupervised step consisting of ISOMAP which is followed by a supervised step using linear Local Fisher Discriminant Analysis (LFDA) [18].…”

Switching Linear Inverse-Regression Model for Tracking Head Pose

“…Not surprisingly, some of the best performing headpose estimation methods rely either on dimensionality reduction followed by regression, [35,32,16,19,4,12,36], or on high-dimensional-to-low-dimensional regression, e.g. [28,22,8,10].…”