Feature Selection in Face Recognition: A Sparse Representation Perspective
Explore this paper's citation graph
Summary
If sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical and the differences in performance between different features become insignificant as the feature-space dimension is sufficiently large.
- Type
- article
- Published
- 2007-01-01
- Cited by
- 199
- References
- 44
- OpenAlex
- https://openalex.org/W94937181
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:545915
Keywords
Pattern recognition (psychology), Sparse approximation, Facial recognition system, Artificial intelligence, Linear subspace
References
- Pattern Classification
- The Johnson-Lindenstrauss Lemma Meets Compressed Sensing
- The AR face database
- Autonomous Military Robotics: Risk, Ethics, and Design
- Learning the parts of objects by non-negative matrix factorization
- Random Projections of Smooth Manifolds
- Atomic Decomposition by Basis Pursuit
- A Component-based Framework for Face Detection and Identification
- Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
- Database-friendly random projections
- The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
- On the Approximability of Minimizing Nonzero Variables or Unsatisfied Relations in Linear Systems
- Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About
- Random projection in dimensionality reduction: applications to image and text data
- Error correction via linear programming
- Multiclass Object Recognition with Sparse, Localized Features
- Effective representation using ICA for face recognition robust to local distortion and partial occlusion
- For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution
- Counting faces of randomly-projected polytopes when the projection radically lowers dimension
Cited by
- A Sparse Coding Based Similarity Measure
- Methods to reduce perturbation effects in compressive sampling
- FACE RECOGNITION BASED ON CUCKOO SEARCH ALGORITHM
- Noise robust digit recognition using sparse representations
- Compressive classification for face recognition
- Frontal face recognition from video via rank-aware multiple measurement vector recovery
- Randomized LU decomposition: An Algorithm for Dictionaries Construction
- Volumetric Data Reduction in a Compressed Sensing Framework
- Generalized Non-linear Sparse Classifier
- Face recognition robust to occlusions
- Gradient-based Laplacian Feature Selection
- Context-Aware Multi-instance Learning Based on Hierarchical Sparse Representation
- A twice face recognition algorithm
- Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization for Block-sparse Compressive Sensing
- A sparse representation-based classifier for in-set bird phrase verification and classification with limited training data
- Adaptive Wallis Filter via Sparse Recognition for Automatic Control Points Extraction
- Class-Discriminative Kernel Sparse Representation-Based Classification Using Multi-Objective Optimization
- Direct extraction and fusion of image pixel features for face identity verification
- Towards 3D Face Recognition in the Real: A Registration-Free Approach Using Fine-Grained Matching of 3D Keypoint Descriptors
- Automatic stellar spectral classification via sparse representations and dictionary learning
Related papers
- Robust Face Recognition via Sparse Representation
- Compressed sensing
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
- Regression Shrinkage and Selection via the Lasso
- Eigenfaces for Recognition
- rm K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
- From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose