Introduction to statistical pattern recognition (2nd ed.)
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Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subspace Analysis Using Random Mixture Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Random Subspaces and Subsampling for 2-D Face Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Sampling for Subspace Face Recognition
International Journal of Computer Vision
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition from a single image per person: A survey
Pattern Recognition
Journal of Cognitive Neuroscience
Face recognition using discriminant locality preserving projections
Image and Vision Computing
Face recognition using optimal linear components of range images
Image and Vision Computing
Random sampling LDA for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Ensembling local learners ThroughMultimodal perturbation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Realtime training on mobile devices for face recognition applications
Pattern Recognition
Semi-supervised classification based on random subspace dimensionality reduction
Pattern Recognition
Semi-supervised ensemble classification in subspaces
Applied Soft Computing
Choosing parameters for random subspace ensembles for fMRI classification
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Incremental face recognition for large-scale social network services
Pattern Recognition
Face recognition using spectrum-based feature extraction
Applied Soft Computing
Proceedings of the CUBE International Information Technology Conference
Proceedings of the CUBE International Information Technology Conference
Astroid shaped DCT feature extraction for enhanced face recognition
Proceedings of the CUBE International Information Technology Conference
Advanced Engineering Informatics
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The small sample size (SSS) and the sensitivity to variations such as illumination, expression and occlusion are two challenging problems in face recognition. In this paper, we propose a novel method, called semi-random subspace (Semi-RS), to simultaneously address the two problems. Different from the traditional random subspace method (RSM) which samples features from the whole pattern feature set in a completely random way, the proposed Semi-RS randomly samples features on each local region (or a sub-image) partitioned from the original face image. More specifically, we first divide a face image into several sub-images in a deterministic way, then construct a set of base classifiers on different randomly sampled feature sets from each sub-image set, and finally combine all base classifiers for the final decision. Experimental results on five face databases (AR, Extended YALE, FERET, Yale and ORL) show that the proposed Semi-RS method is effective, relatively robust to illumination and occlusion, etc., and also suitable to slight variations in pose angle and the scenario of one training sample per person. In addition, kappa-error diagram, which is used to analyze the diversity of algorithm, reveals that Semi-RS constructs more diverse base classifiers than other methods, and also explains why Semi-RS can yield better performance than RSM and V-SpPCA.