Statistical color models with application to skin detection
International Journal of Computer Vision
Multilinear Independent Components Analysis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Graph Embedded Analysis for Head Pose Estimation
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Journal of Cognitive Neuroscience
Learning a Person-Independent Representation for Precise 3D Pose Estimation
Multimodal Technologies for Perception of Humans
Locating nose-tips and estimating head poses in images by tensorposes
IEEE Transactions on Circuits and Systems for Video Technology
Visual Intention Detection for Wheelchair Motion
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
A new representation method of head images for head pose estimation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Exploiting perception for face analysis: image abstraction for head pose estimation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation
International Journal of Computer Vision
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This paper introduces our head pose estimation system that localizes nose-tip of the faces and estimate head poses in studio quality pictures. After the nose-tip in the training data are manually labeled, the appearance variation caused by head pose changes is characterized by tensor model. Given images with unknown head pose and nose-tip location, the nose-tip of the face is localized in a coarse-to-fine fashion, and the head pose is estimated simultaneously by the head pose tensor model. The image patches at the localized nose tips are then cropped and sent to two other head pose estimators based on LEA and PCA techniques. We evaluated our system on the Pointing'04 head pose image database. With the nose-tip location known, our head pose estimators can achieve 94 - 96% head pose classification accuracy(within ±15°). With nose-tip unknown, we achieves 85% nose-tip localization accuracy (within 3 pixels from the ground truth), and 81 - 84% head pose classification accuracy(within ±15°).