Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Vision: From Images to Face Recognition
Dynamic Vision: From Images to Face Recognition
Component-based LDA Method for Face Recognition with One Training Sample
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Regularized discriminant analysis for the small sample size problem in face recognition
Pattern Recognition Letters
Journal of Cognitive Neuroscience
IEEE Transactions on Image Processing
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Discriminant feature extraction using dual-objective optimization model
Pattern Recognition Letters
Computer Vision and Image Understanding
On solving the face recognition problem with one training sample per subject
Pattern Recognition
A discriminant analysis using composite features for classification problems
Pattern Recognition
Personal recognition based on an image of the palmar surface of the hand
Pattern Recognition
Fast linear discriminant analysis using binary bases
Pattern Recognition Letters
Dynamic training using multistage clustering for face recognition
Pattern Recognition
CompSysTech '07 Proceedings of the 2007 international conference on Computer systems and technologies
Kernel quadratic discriminant analysis for small sample size problem
Pattern Recognition
Kernel quadratic discriminant analysis for small sample size problem
Pattern Recognition
Regularization Versus Dimension Reduction, Which Is Better?
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
ICA-based neighborhood preserving analysis for face recognition
Computer Vision and Image Understanding
Direct kernel neighborhood discriminant analysis for face recognition
Pattern Recognition Letters
Spatially Smooth Subspace Face Recognition Using LOG and DOG Penalties
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
A bi-modal face recognition framework integrating facial expression with facial appearance
Pattern Recognition Letters
Audio-video biometric recognition for non-collaborative access granting
Journal of Visual Languages and Computing
IEEE Transactions on Neural Networks
Rapid and brief communication: An efficient kernel discriminant analysis method
Pattern Recognition
The face recognition algorithm based on offset difference of double subspace
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Using sparse regression to learn effective projections for face recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Regularized locality preserving projections and its extensions for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Efficient update of the covariance matrix inverse in iterated linear discriminant analysis
Pattern Recognition Letters
Improved direct LDA and its application to DNA microarray gene expression data
Pattern Recognition Letters
Short communication: Diagnosis of bladder cancers with small sample size via feature selection
Expert Systems with Applications: An International Journal
Face recognition based on the multi-scale local image structures
Pattern Recognition
An improvement in feature feedback using R-LDA with application to Yale database
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Computers and Electrical Engineering
Improvement on null space LDA for face recognition: a symmetry consideration
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
A comparative study of skin-color models
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Face recognition – combine generic and specific solutions
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Feature extraction using a fast null space based linear discriminant analysis algorithm
Information Sciences: an International Journal
Feature extraction using fuzzy maximum margin criterion
Neurocomputing
A two-stage linear discriminant analysis for face-recognition
Pattern Recognition Letters
Novel Fisher discriminant classifiers
Pattern Recognition
Modular discriminant analysis and its applications
Artificial Intelligence Review
Dynamic action recognition based on dynemes and Extreme Learning Machine
Pattern Recognition Letters
Global plus local: A complete framework for feature extraction and recognition
Pattern Recognition
Improving fusion with optimal weight selection in Face Recognition
Integrated Computer-Aided Engineering
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It is well-known that the applicability of linear discriminant analysis (LDA) to high-dimensional pattern classification tasks such as face recognition often suffers from the so-called ''small sample size'' (SSS) problem arising from the small number of available training samples compared to the dimensionality of the sample space. In this paper, we propose a new LDA method that attempts to address the SSS problem using a regularized Fisher's separability criterion. In addition, a scheme of expanding the representational capacity of face database is introduced to overcome the limitation that the LDA-based algorithms require at least two samples per class available for learning. Extensive experiments performed on the FERET database indicate that the proposed methodology outperforms traditional methods such as Eigenfaces and some recently introduced LDA variants in a number of SSS scenarios.