For the last decades, manifold learning has shown its advantage of efficient non-linear dimensionality reduction in data analysis. Based on the assumption that informative and discriminative representation of the data lies on a low-dimensional smooth manifold which implicitly embedded in the original high-dimensional space, manifold learning aims to learn the low-dimensional representation foll...

This paper presents our participation in the CLEAR 07 evaluation workshop head pose estimation tasks where two head pose estimation tasks were to be addressed. The first task estimates head poses with respect to (w.r.t.) a single camera capturing people seated in a meeting room scenario. The second task consisted of estimating the head pose of people moving in a room from four cameras w.r.t. a ...

Head pose estimation is an important task for many applications such as human-computer interaction and human action understanding since a person’s head direction has an important role in representing his/her intention. In this paper, we propose a real-time head pose estimation method with stereo vision, which does not stress users and is easily applied to a lot of users. We use the degree of th...

Traditional appearance-based head pose estimation methods use the holistic face appearance as input and then employ statistic learning methods to extract low dimension features for classification. However, the face appearance may be more related to the unique identity of an individual rather than head poses. In this paper, we propose an image descriptor, covariance of oriented gradients (COG), ...

In video-surveillance and ambient intelligence applications, head-pose estimation is an important and challenging task. Basically, the problem lies in assessing the pose of the head according to three reference angles, that indicate the head rotation with respect to three orthogonal axes, and are named roll, yaw, and pitch angles. The problem becomes particularly difficult if only 2D video-sequ...

In this paper we propose supervised initialisation scheme for cascaded face alignment based on explicit head pose estimation. We first investigate the failure cases of most state of the art face alignment approaches and observe that these failures often share one common global property, i.e. the head pose variation is usually large. Inspired by this, we propose a deep convolutional network mode...