Non-photorealistic computer graphics: modeling, rendering, and animation
Non-photorealistic computer graphics: modeling, rendering, and animation
Stylization and abstraction of photographs
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Wide-Range, Person- and Illumination-Insensitive Head Orientation Estimation
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Graph Embedded Analysis for Head Pose Estimation
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Using NPR to evaluate perceptual shape cues in dynamic environments
Proceedings of the 5th international symposium on Non-photorealistic animation and rendering
Head Pose Estimation in Computer Vision: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
How well do line drawings depict shape?
ACM SIGGRAPH 2009 papers
Head Pose estimation on low resolution images
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Evaluation of head pose estimation for studio data
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Supervised local subspace learning for continuous head pose estimation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
| Hi-index | 0.00 |
We present an algorithm to estimate the pose of a human head from a single, low resolution image in real time. It builds on the fundamentals of human perception i.e. abstracting the relevant details from visual cues. Most images contain far too many cues than what are required for estimating human head pose. Thus, we use non-photorealistic rendering to eliminate irrelevant details like expressions from the picture and accentuate facial features critical to estimating head pose. The maximum likelihood pose range is then estimated by training a classifier on scaled down abstracted images. The results are extremely encouraging especially when compared with other recent methods. Moreover the algorithm is robust to illumination, expression, identity and resolution.