Head pose estimation with one camera, in uncalibrated environments

Authors:
Stylianos Asteriadis;Kostas Karpouzis;Stefanos Kollias
Affiliations:
National Technical University of Athens, Athens, Greece;National Technical University of Athens, Athens, Greece;National Technical University of Athens, Athens, Greece
Venue:
Proceedings of the 2010 workshop on Eye gaze in intelligent human machine interaction
Year:
2010

Citing 8
Cited 1

Fast, Reliable Head Tracking under Varying Illumination: An Approach Based on Registration of Texture-Mapped 3D Models

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Computer and Robot Vision

Computer and Robot Vision
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FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Tracking Focus of Attention in Meetings

ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Pose Robust Face Tracking by Combining Active Appearance Models and Cylinder Head Models

International Journal of Computer Vision
Head Pose Estimation in Computer Vision: A Survey

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A natural head pose and eye gaze dataset

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Evaluating dual-view perceptual issues in handheld augmented reality: device vs. user perspective rendering

Proceedings of the 15th ACM on International conference on multimodal interaction

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Abstract

Head pose together with eye gaze are a reliable indication regarding the estimate of the focus of attention of a person standing in front of a camera, with applications ranging from driver's attention estimation to meeting environments. As gaze indication, eye gaze in non-intrusive or non highly specialized environments is, most times, difficult to detect and, when possible, combination with head pose is necessary. Also, in order to successfully track the rotation angles of the head, a priori knowledge regarding the equipment setup parameters is needed, or specialized hardware, that can be intrusive is required. Here, we propose a novel facial feature tracker that uses Distance Vector Fields (DVFs) and, combined with a new technique for face tracking, successfully detects facial feature positions during an image sequence and estimates head pose parameters. No a priori knowledge regarding camera or environmental parameters is needed for our technique.