Visual reconstruction
An introduction to genetic algorithms
An introduction to genetic algorithms
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Robustly estimating changes in image appearance
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Early Visual Learning
Computer Vision and Human-Computer Interaction
Computer Vision and Human-Computer Interaction
Dynamic Vision: From Images to Face Recognition
Dynamic Vision: From Images to Face Recognition
Transformation-Invariant Clustering Using the EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
View Alignment with Dynamically Updated Affine Tracking
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
A Probabilistic Framework for Rigid and Non-Rigid Appearance Based Tracking and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Fast object recognition in noisy images using simulated annealing
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Eigenfiltering for Flexible Eigentracking (EFE)
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Automatic Learning of Appearance Face Models
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Mixtures of Eigenfeatures for Real-Time Structure from Texture
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Journal of Cognitive Neuroscience
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wizard-of-Oz test of ARTUR: a computer-based speech training system with articulation correction
Proceedings of the 7th international ACM SIGACCESS conference on Computers and accessibility
Robust probabilistic projections
ICML '06 Proceedings of the 23rd international conference on Machine learning
IEEE Transactions on Knowledge and Data Engineering
Subspace manifold learning with sample weights
Image and Vision Computing
Efficient illumination independent appearance-based face tracking
Image and Vision Computing
Robust least-squares image matching in the presence of outliers
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Principal Component Analysis (PCA) has been successfully applied to construct linear models of shape, graylevel, and motion. In particular, PCA has been widely used to model the variation in the appearance of people's faces. We extend previous work on facial modeling for tracking faces in video sequences as they undergo significant changes due to facial expressions. Here we develop person-specific facial appearance models (PSFAM), which use modular PCA to model complex intra-person appearance changes. Such models require aligned visual training data; in previous work, this has involved a time consuming and errorprone hand alignment and cropping process. Instead, we introduce parameterized component analysis to learn a subspace that is invariant to affine (or higher order) geometric transformations. The automatic learning of a PSFAM given a training image sequence is posed as a continuous optimization problem and is solved with a mixture of stochastic and deterministic techniques achieving sub-pixel accuracy. We illustrate the use of the 2D PSFAM model with several applications including video-conferencing, realistic avatar animation and eye tracking.