Real-time gaze estimation using a Kinect and a HD webcam

In human-computer interaction, gaze orientation is an important and promising source of information to demonstrate the attention and focus of users. Gaze detection can also be an extremely useful metric for analysing human mood and affect. Furthermore, gaze can be used as an input method for human-computer interaction. However, currently real-time and accurate gaze estimation is still an open problem. In this paper, we propose a simple and novel estimation model of the real-time gaze direction of a user on a computer screen. This method utilises cheap capturing devices, a HD webcam and a Microsoft Kinect. We consider that the gaze motion from a user facing forwards is composed of the local gaze motion shifted by eye motion and the global gaze motion driven by face motion. We validate our proposed model of gaze estimation and provide experimental evaluation of the reliability and the precision of the method

Webcam-based Visual Gaze Estimation

Abstract. In this paper we combine a state of the art eye center locator and a new eye corner locator into a system which estimates the visual gaze of a user in a controlled environment (e.g. sitting in front of a screen). In order to reduce to a minimum the computational costs, the eye corner locator is built upon the same technology of the eye center locator, tweaked for the specific task. If high mapping precision is not a priority of the application, we claim that the system can achieve acceptable accuracy without the requirements of additional dedicated hardware. We believe that this could bring new gaze based methodologies for human-computer interactions into the mainstream.

Rendering Optimizations Guided by Head-Pose Estimates and Their Uncertainty

Abstract. In virtual environments, head pose and/or eye-gaze estimation can be employed to improve the visual experience of the user by enabling adaptive level of detail during rendering. In this study, we present a real-time system for rendering complex scenes in an immersive virtual environment based on head pose estimation and perceptual level of detail. In our system, the position and orientation of the head are estimated using stereo vision approach and markers placed on a pair of glasses used to view images projected on a stereo display device. The main innovation of our work is the incorporation of uncertainty estimates to improve the visual experience perceived by the user. The estimated pose and its uncertainty are used to determine the desired level of detail for different parts of the scene based on criteria originating from physiological and psychological aspects of human vision. Subject tests have been performed to evaluate our approach.