An Algorithm for Real-Time Stereo Vision Implementation of Head Pose and Gaze Direction Measurement
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Fast Stereo-Based Head Tracking for Interactive Environments
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Model-Based Head Pose Tracking With Stereovision
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Head Pose Estimation using Fisher Manifold Learning
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Pose Estimation using 3D View-Based Eigenspaces
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Synergistic Face Detection and Pose Estimation with Energy-Based Models
The Journal of Machine Learning Research
Head Pose Estimation in Computer Vision: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Head pose estimation using stereo vision for human-robot interaction
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Realtime performance-based facial animation
ACM SIGGRAPH 2011 papers
Real time head pose estimation from consumer depth cameras
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Real time head pose estimation with random regression forests
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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In this paper, we propose a system to estimate head poses only using depth information in real-time. An optimization method based on 3D model fitting is developed. We iteratively minimize the distance between source and target point clouds of a user's head. The method give fully real-time responses (30fps) without the GPU speedup. We adopt a commodity depth sensor named Microsoft Kinect as well as Asus Xtion, and use the depth image as the only input so that our system will not be affected by illumination variations. However, the simplicity of this acquisition device comes at the cost of frequent noises in the acquired data. We demonstrate that 6 degrees of freedom real-time head motion tracking in 3D space can be achieved with such noisy depth data.