Non-linear dimensionality reduction and sparse representation models for facial analysis. (Réduction de la dimension non-linéaire et modèles de la représentations parcimonieuse pour l'analyse du visage)
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Summary
A novel non-linear embedding method is introduced, the Kernel Similarity Principal Component Analysis (KS-PCA), into Active Appearance Models, in order to model face appearances under variable illumination and demonstrate the effectiveness of the sparsity as a prior for patch-based illumination normalization for face images.
- Type
- preprint
- Published
- 2014-02-20
- Cited by
- 1
- References
- 250
- Access
- Open access
- OpenAlex
- https://openalex.org/W658579991
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:42613926
Keywords
Sparse approximation, Pattern recognition (psychology), Artificial intelligence, Dimensionality reduction, Principal component analysis
References
- Kernel Methods in Bioengineering, Signal And Image Processing
- Color TV: total variation methods for restoration of vector-valued images
- Procrustes methods in the statistical analysis of shape
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- Sparse representations for image classification: learning discriminative and reconstructive non-parametric dictionaries
- Compressed Sensing
- JUST RELAX: CONVEX PROGRAMMING METHODS FOR SUBSET SELECTION AND SPARSE APPROXIMATION
- Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges
- JPEG: Still Image Data Compression Standard
- Head pose estimation using Fisher Manifold learning
- Real-time head tracking and 3D pose estimation from range data
- A Tutorial on Principal Component Analysis
- Estimating facial pose from a sparse representation [face recognition applications]
- Shift-Invariant Dictionary Learning For Sparse Representations: Extending K-Svd
- Generative Interpretation of Medical Images
- Automatic interpretation of human faces and hand gestures using flexible models.
- Real-time non-rigid driver head tracking for driver mental state estimation
- Face recognition by elastic bunch graph matching
- A Closed-Form Solution to Non-Rigid Shape and Motion Recovery
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