Two-stage optimal component analysis
Computer Vision and Image Understanding
Independent component analysis-based defect detection in patterned liquid crystal display surfaces
Image and Vision Computing
Saliency model-based face segmentation and tracking in head-and-shoulder video sequences
Journal of Visual Communication and Image Representation
Learning a Person-Independent Representation for Precise 3D Pose Estimation
Multimodal Technologies for Perception of Humans
Head Pose Estimation Based on Tensor Factorization
Neural Information Processing
IEEE Transactions on Circuits and Systems for Video Technology
Using mutual information to indicate facial poses in video sequences
Proceedings of the ACM International Conference on Image and Video Retrieval
Synchronized submanifold embedding for person-independent pose estimation and beyond
IEEE Transactions on Image Processing
Independent components extraction from image matrix
Pattern Recognition Letters
Classification of 3-D objects and faces employing view-based clusters
Computers and Electrical Engineering
3D face pose estimation based on multi-template AAM
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Robust frontal view search using extended manifold learning
Journal of Visual Communication and Image Representation
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An independent component analysis (ICA) based approach is presented for learning view-specific subspace representations of the face object from multiview face examples. ICA, its variants, namely independent subspace analysis (ISA) and topographic independent component analysis (TICA), take into account higher order statistics needed for object view characterization. In contrast, principal component analysis (PCA), which de-correlates the second order moments, can hardly reveal good features for characterizing different views, when the training data comprises a mixture of multiview examples and the learning is done in an unsupervised way with view-unlabeled data. We demonstrate that ICA, TICA, and ISA are able to learn view-specific basis components unsupervisedly from the mixture data. We investigate results learned by ISA in an unsupervised way closely and reveal some surprising findings and thereby explain underlying reasons for the emergent formation of view subspaces. Extensive experimental results are presented.