Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Global Dimensionality of Face Space
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Learning controls for blend shape based realistic facial animation
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Registration of Expressions Data using a 3D Morphable Model
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Data-Driven Feature-Based 3D Face Synthesis
3DIM '07 Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling
Numerical Geometry of Non-Rigid Shapes
Numerical Geometry of Non-Rigid Shapes
A Region Ensemble for 3-D Face Recognition
IEEE Transactions on Information Forensics and Security
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In this paper, we explore region-based 3D representations of the human face. We begin by noting that although they serve as a key ingredient in many state-of-the-art 3D face reconstruction algorithms, very little research has gone into devising strategies for optimally designing them. In fact, the great majority of such models encountered in the literature is based on manual segmentations of the face into subregions. We propose algorithms that are capable of automatically finding the optimal subdivision given a training set and the number of desired regions. The generality of the segmentation approach is demonstrated on examples from the TOSCA database, and a cross-validation experiment on facial data shows that part-based models designed using the proposed algorithms are capable of outperforming alternative segmentations w.r.t. reconstruction accuracy.