Machine Learning
EM enhancement of 3D head pose estimated by point at infinity
Image and Vision Computing
Head Pose Estimation in Computer Vision: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust real-time 3D head pose estimation from range data
Pattern Recognition
Head Pose Estimation Based on Random Forests for Multiclass Classification
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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
Real time head pose estimation with random regression forests
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Supervised local subspace learning for continuous head pose estimation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
3D aided face recognition across pose variations
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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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 center of a slice to nose tip. Random regression forests are employed to map discriminative integral slice features to continuous head poses, given the advantage that they can maintain accuracy when a large proportion of the data is missing. Experimental results on the ETH database demonstrate that the proposed method is more accurate than state-of-the-art methods for head pose estimation.