A Generative Shape Regularization Model for Robust Face Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Multi-View Face Alignment Using 3D Shape Model for View Estimation
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Locating Facial Features and Pose Estimation Using a 3D Shape Model
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Towards 3D-aided profile-based face recognition
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Non-rigid face tracking with enforced convexity and local appearance consistency constraint
Image and Vision Computing
Learning a generic 3D face model from 2D image databases using incremental Structure-from-Motion
Image and Vision Computing
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Adding facial actions into 3D model search to analyse behaviour in an unconstrained environment
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Personalized 3D-aided 2D facial landmark localization
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Manifold estimation in view-based feature space for face synthesis across poses
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Synthesis of a face image at a desired pose from a given pose
Pattern Recognition Letters
Image and Vision Computing
Salient and non-salient fiducial detection using a probabilistic graphical model
Pattern Recognition
| Hi-index | 0.00 |
We present an approach for aligning a 3D deformable model to a single face image. The model consists of a set of sparse 3D points and the view-based patches associated with every point. Assuming a weak perspective projection model, our algorithm iteratively deforms the model and ad- justs the 3D pose to fit the image. As opposed to previous approaches, our algorithm starts the fitting without resort- ing to manual labeling of key facial points. And it makes no assumptions about global illumination or surface prop- erties, so it can be applied to a wide range of imaging con- ditions. Experiments demonstrate that our approach can effectively handle unseen faces with a variety of pose and illumination variations.