A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Towards Correlation-Based Matching Algorithms that are Robust Near Occlusions
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Multilevel Quadratic Variation Minimization for 3D Face Modeling and Virtual View Synthesis
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
An active model for facial feature tracking
EURASIP Journal on Applied Signal Processing
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Subspace analysis and optimization for AAM based face alignment
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Classifier combination for face localization in color images
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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The modelisation of human faces from images can be done by the mean of morphable models such as AAMs. However, fitting such models without previous estimations is a challenging task. Shape estimation needs a close texture reference, and texture approximation requires shape knowledge. In this paper, we address the efficiency of sampling and generic encoding in regard to the shape alignment accuracy, without previous texture approximation. The hybrid method we propose is based on a relative barycentric resampling of the face model, a generic coding of the reference texture and a normalized cost function. We also present a new warping function definition to simplify the initial global parameter estimation. These new subsampling and encoding frameworks improve the accuracy of facial shape alignment in unconstrained cases.