EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Orthogonal Tensor Decompositions
SIAM Journal on Matrix Analysis and Applications
Incremental Singular Value Decomposition of Uncertain Data with Missing Values
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Online Learning of Probabilistic Appearance Manifolds for Video-Based Recognition and Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Tracking via Collaboration of Generic and Specific Models
IEEE Transactions on Image Processing
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A novel incremental multi-view face tracking algorithm is proposed in the graphic model, which includes a general view manifold and specific incremental face model. We extend a general view manifold to the state-space model of face tracking to represent the view continuity and nonlinearity in the video data. Particularly, a global constraint on the overall appearance of the tracked multi-view faces is defined based on the point-to-manifold distance to avoid drifting. This novel face tracking model can successfully track faces under unseen views, and experimental results proved the new method is superior to two state-of-art algorithms for multi-view face tracking.