Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Face recognition from one example view
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition from a single image per person: A survey
Pattern Recognition
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
On the Dimensionality of Face Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
A unified framework for semi-supervised dimensionality reduction
Pattern Recognition
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Comments on “Efficient and Robust Feature Extraction by Maximum Margin Criterion”
IEEE Transactions on Neural Networks
Face recognition using sparse representations and manifold learning
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Experiments on lattice independent component analysis for face recognition
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Graph optimization for dimensionality reduction with sparsity constraints
Pattern Recognition
Nearest-neighbor classifier motivated marginal discriminant projections for face recognition
Frontiers of Computer Science in China
Adaptive discriminant learning for face recognition
Pattern Recognition
Double linear regressions for single labeled image per person face recognition
Pattern Recognition
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Single training image face recognition is one of the main challenges to appearance-based pattern recognition techniques. Many classical dimensionality reduction methods such as LDA have achieved success in face recognition field, but cannot be directly used to the single training image scenario. Recent graph-based semi-supervised dimensionality reduction (SSDR) provides a feasible strategy to deal with such problem. However, most of the existing SSDR algorithms such as semi-supervised discriminant analysis (SDA) are locality-oriented and generally suffer from the following issues: (1) they need a large number of unlabeled training samples to estimate the manifold structure in data, but such extra samples may not be easily obtained in a given face recognition task; (2) they model the local geometry of data by the nearest neighbor criterion which generally fails to obtain sufficient discriminative information due to the high-dimensionality of face image space; (3) they construct the underlying adjacency graph (or data-dependent regularizer) using a fixed neighborhood size for all the sample points without considering the actual data distribution. In this paper, we develop a new graph-based SSDR algorithm called sparsity preserving discriminant analysis (SPDA) to address these problems. More specifically, (1) the graph in SPDA is constructed by sparse representation, and thus the local structure in data is automatically modeled instead of being manually predefined. (2) With the natural discriminative power of sparse representation, SPDA can remarkably improve recognition performance only resorting to very few extra unlabeled samples. (3) A simple ensemble strategy is developed to accelerate graph construction, which results in an efficient ensemble SPDA algorithm. Extensive experiments on both toy and real face data sets are provided to validate the feasibility and effectiveness of the proposed algorithm.