In this paper, we present a system to track the horizontal head orientation of a lecturer in a smart seminar room, which is equipped with several cameras. We automatically detect and track the face of the lecturer and use neural networks to classify his or her face orientation in each camera view. By combining the single estimates of the speaker’s head orientation from multiple cameras into one...

People often look at objects and people with which they are likely to interact. The first step for computer systems to adapt to the user and to improve interaction and with people is to locate where they are, and especially the location of their faces on the image. The next step is to track their focus of attention. For this reason, we are interested in techniques for estimating and tracking ga...

We present an augmented reality interface with markerless human body interaction. It consists of 3D motion capture of the human body and the processing of 3D human poses for augmented reality applications. A monocular camera is used to acquire the images of the user’s motion for 3D pose estimation. In the proposed technique, a graphical 3D human model is first constructed. Its projection on a v...

A method of manifold representation for human faces with pose variations is proposed. Our model consists of mappings between 3D head angles and facial images separately represented in shape and texture, via sub-space models spanned by principal components (PCs). Explicit mappings to and from 3D head angles are used as processes of pose estimation and transformation, respectively. Generalization...

This paper presents a generic method for addressing the issue of 3D model-based head pose estimation. The method proposed relies on the downhill simplex optimization method and on the combination of motion and texture features. A proper initialization based on a block matching procedure associated with 3D/2D matching depending on texture and optical flow information leads to an accurate recover...

We present an algorithm for estimation of head orientation, given cropped images of a subject’s head from any viewpoint. Our algorithm handles dramatic changes in illumination, applies to many people without per-user initialization, and covers a wider range (e.g., side and back) of head orientations than previous algorithms. The algorithm builds an ellipsoidal model of the head, where points on...

Estimating a driver’s head pose is an important task in driver-assistance systems because it can provide information about where a driver is looking, thereby giving useful cues about the status of the driver (i.e., paying proper attention, fatigued, etc.). This study proposes a system for estimating the head pose using monocular images, which includes a novel use of backprojection. The system c...

Within the past decade, many computational approaches have been developed to estimate gaze directions of persons based on their facial appearance. Most researchers used common face datasets with only a limited representation of different head poses to train and verify their algorithms. Moreover, in most datasets, faces have neither a defined gaze direction, nor do they incorporate different com...

Human head pose and gaze directions have traditionally been studied for expression and face recognition, and human computer interaction [6]. Accurate estimations of either head or gaze direction can provide useful information for the inference of a person’s intent and behaviour. However, most existing techniques rely upon medium to high resolution images captured under well controlled condition...

We demonstrate real-time face tracking and pose estimation in an unconstrained ooce environment with an active foveated camera. Using vision routines previously implemented for an interactive environment, we determine the spatial location of a user's head and guide an active camera to obtain foveated images of the face. Faces are analyzed using a set of eigenspaces indexed over both pose and wo...