A new approach for face recognition by sketches in photos
Signal Processing
Dynamic Exponential Family Matrix Factorization
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Gabor texture in active appearance models
Neurocomputing
Distance approximating dimension reduction of Riemannian manifolds
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A review of active appearance models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Maximum margin criterion with tensor representation
Neurocomputing
View-based 3D model retrieval with probabilistic graph model
Neurocomputing
A unified tensor level set for image segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
A comparative study of active appearance model annotation schemes for the face
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
A survey of multilinear subspace learning for tensor data
Pattern Recognition
Hierarchical visual event pattern mining and its applications
Data Mining and Knowledge Discovery
Expression transfer for facial sketch animation
Signal Processing
Theoretical Analysis of Bayesian Matrix Factorization
The Journal of Machine Learning Research
Computer vision for fruit harvesting robots state of the art and challenges ahead
International Journal of Computational Vision and Robotics
Accelerated singular value thresholding for matrix completion
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple kernel local Fisher discriminant analysis for face recognition
Signal Processing
Biview face recognition in the shape-texture domain
Pattern Recognition
A Comprehensive Survey to Face Hallucination
International Journal of Computer Vision
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Effectively modeling a collection of three-dimensional (3-D) faces is an important task in various applications, especially facial expression-driven ones, e.g., expression generation, retargeting, and synthesis. These 3-D faces naturally form a set of second-order tensors-one modality for identity and the other for expression. The number of these second-order tensors is three times of that of the vertices for 3-D face modeling. As for algorithms, Bayesian data modeling, which is a natural data analysis tool, has been widely applied with great success; however, it works only for vector data. Therefore, there is a gap between tensor-based representation and vector-based data analysis tools. Aiming at bridging this gap and generalizing conventional statistical tools over tensors, this paper proposes a decoupled probabilistic algorithm, which is named Bayesian tensor analysis (BTA). Theoretically, BTA can automatically and suitably determine dimensionality for different modalities of tensor data. With BTA, a collection of 3-D faces can be well modeled. Empirical studies on expression retargeting also justify the advantages of BTA.