This paper describes a visual interface that infers the intention of a person to travel in a desired direction from head movements. A rotation of the head indicates the intent to change direction. The context for which this solution is intended is that of wheelchair bound individuals. This paper describes work in progress that provides a proof of concept tested on static images where head pose ...

In this paper, we propose a new tracking system based on a stochastic filtering framework for reliably estimating the 3D pose of a user’s head in real-time. Our system estimates the pose of a user’s head in each image frame whose 3D model is automatically obtained at an initialization step. In particular, our estimation method is designed to control the diffusion factor of a motion model adapti...

Recently, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we tackle the problem of head pose estimation through a Convolutional Neural Network (CNN). Differently from other proposals in the literature, the described system is able to work directly and based only on raw depth data. Moreover, the head pose estimation is solved as a reg...

The correct estimation of the head pose is a problem of the great importance for many applications. For instance, it is an enabling technology in automotive for driver attention monitoring. In this paper, we tackle the pose estimation problem through a deep learning network working in regression manner. Traditional methods usually rely on visual facial features, such as facial landmarks or nose...

Automatic head pose estimation is useful in human computer interaction and biometric recognition. However, it is a very challenging problem. To achieve robust for head pose estimation, a novel method based on depth images is proposed in this paper. The bilateral symmetry of face is utilized to design a discriminative integral slice feature, which is presented as a 3D vector from the geometric c...

Head pose estimation is an important problem in the field of computer vision and facial analysis. We model the problem of head pose estimation as a regression problem, where the three rotation angles (yaw, pitch, roll) are functions of the face appearance. We make use of that fact and learn the appearance of the face using a tree cascade of sparse MultiVariate Relevance Vector Machines (MVRVM)....

We present a robust front-end pose classification/estimation procedure to be used in face recognition scenarios. A novel discriminative feature description that encodes underlying shape well and is insensitive to illumination and other common variations in facial appearance, such as skin colour etc., is proposed. Using such features we generate a pose similarity feature space (PSFS) that turns ...

Various computer vision problems and applications rely on an accurate, fast head pose estimator. We model head pose estimation as a regression problem. We show that it is possible to use the appearance of the facial image as a feature which depicts the pose variations. We use a parametrized Multi-Variate Relevance Vector Machine (MVRVM) to learn the three rotation angles of the face (yaw, pitch...

We perform head pose estimation solely based on the nose region as input, extracted from 2D images in unconstrained environments. Such information is useful for many face analysis applications, such as recognition, reconstruction, alignment, tracking and expression recognition. Using the nose region has advantages over using the whole face; not only it is less likely to be occluded by acesssori...