Orthogonal discriminant improved local tangent space alignment based feature fusion for face recognition
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Summary
A novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed which can preserve local geometry structure and maximize the margin between different classes simultaneously.
- Type
- article
- Published
- 2013-06-01
- Cited by
- 1
- References
- 21
- OpenAlex
- https://openalex.org/W98268252
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:51778619
Keywords
Pattern recognition (psychology), Nonlinear dimensionality reduction, Artificial intelligence, Dimensionality reduction, Tangent space
References
- Proceedings of the 17th European Signal Processing Conference
- The dual-tree complex wavelet transform: A new efficient tool for image restoration and enhancement
- An improved local tangent space alignment method for manifold learning
- The CMU Pose, Illumination, and Expression Database
- Feature extraction using constrained maximum variance mapping
- Feature extraction using orthogonal discriminant local tangent space alignment
- Linear local tangent space alignment and application to face recognition
- Locally linear discriminant embedding: An efficient method for face recognition
- Face recognition using eigenfaces
- Neighborhood preserving embedding
- Efficient and robust feature extraction by maximum margin criterion
- SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
- Construction of Hilbert Transform Pairs of Wavelet Bases and Gabor-Like Transforms
- Face recognition using Laplacianfaces
- Orthogonal neighborhood preserving discriminant analysis for face recognition
- Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
- Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
- Eigenfaces vs . Fisherfaces : Recognition Using Class Speci c Linear Projection
- Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold
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