Implicit fairing of irregular meshes using diffusion and curvature flow
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
ACM SIGGRAPH 2005 Papers
Salient geometric features for partial shape matching and similarity
ACM Transactions on Graphics (TOG)
A 3D Facial Expression Database For Facial Behavior Research
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust 3D Face Recognition by Local Shape Difference Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface regions of interest for viewpoint selection
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Saliency-guided integration of multiple scans
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Head pose is an important indicator of a person's attention, gestures, and communicative behavior with applications in human-computer interaction, multimedia, and vision systems. Robust head pose estimation is a prerequisite for spontaneous facial biometrics-related applications. However, most previous head pose estimation methods do not consider the facial expression and hence are more likely to be influenced by the facial expression. In this paper, we develop a saliency-guided 3D head pose estimation on 3D expression models. We address the problem of head pose estimation based on a generic model and saliency guided segmentation on a Laplacian fairing model. We propose to perform mesh Laplacian fairing to remove noise and outliers on the 3D facial model. The salient regions are detected and segmented from the model. The salient region Iterative Closest Point (ICP) then register the test face model with the generic head model. The algorithms for pose estimation are evaluated through both static and dynamic 3D facial databases. Overall, the extensive results demonstrate the effectiveness and accuracy of our approach.