Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Object Classes and Image Synthesis From a Single Example Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of probabilistic principal component analyzers
Neural Computation
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Empirical Performance Analysis of Linear Discriminant Classifiers
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
An Investigation into Face Pose Distributions
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Identification across Different Poses and Illuminations with a 3D Morphable Model
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Learning a Locality Preserving Subspace for Visual Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Appearance-Based Face Recognition and Light-Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Independent component analysis in a facial local residue space
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally adaptive classification piloted by uncertainty
ICML '06 Proceedings of the 23rd international conference on Machine learning
On solving the face recognition problem with one training sample per subject
Pattern Recognition
Locality preserving CCA with applications to data visualization and pose estimation
Image and Vision Computing
Boosted manifold principal angles for image set-based recognition
Pattern Recognition
Expert Systems with Applications: An International Journal
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A kernel optimization method based on the localized kernel Fisher criterion
Pattern Recognition
Letters: Kernel subclass discriminant analysis
Neurocomputing
Computers in Biology and Medicine
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
A Viewpoint Invariant, Sparsely Registered, Patch Based, Face Verifier
International Journal of Computer Vision
Face Pose Estimation and Synthesis by 2D Morphable Model
Computational Intelligence and Security
On Using Dimensionality Reduction Schemes to Optimize Dissimilarity-Based Classifiers
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Face Recognition Using Parabola Edge Map
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition
International Journal of Computer Vision
Pose-Invariant Face Matching Using MRF Energy Minimization Framework
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Expression-invariant face recognition with constrained optical flow warping
IEEE Transactions on Multimedia
Secure open source collaboration: an empirical study of linus' law
Proceedings of the 16th ACM conference on Computer and communications security
Probabilistic learning for fully automatic face recognition across pose
Image and Vision Computing
A pre-clustering technique for optimizing subclass discriminant analysis
Pattern Recognition Letters
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Block-wise 2D kernel PCA/LDA for face recognition
Information Processing Letters
Strengthening the empirical analysis of the relationship between Linus' Law and software security
Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Computer Vision and Image Understanding
Local binary LDA for face recognition
BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
Self-tuned Evolution-COnstructed features for general object recognition
Pattern Recognition
Operators for transforming kernels into quasi-local kernels that improve SVM accuracy
Journal of Intelligent Information Systems
Gabor feature based face recognition using supervised locality preserving projection
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Inter-modality face recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Combining geometric and gabor features for face recognition
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A supervised non-linear dimensionality reduction approach for manifold learning
Pattern Recognition
Coupling adaboost and random subspace for diversified fisher linear discriminant
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Class dependent factor analysis and its application to face recognition
Pattern Recognition
Local maximal margin discriminant embedding for face recognition
Journal of Visual Communication and Image Representation
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We present a novel method of nonlinear discriminant analysis involving a set of locally linear transformations called "Locally Linear Discriminant Analysis (LLDA).驴 The underlying idea is that global nonlinear data structures are locally linear and local structures can be linearly aligned. Input vectors are projected into each local feature space by linear transformations found to yield locally linearly transformed classes that maximize the between-class covariance while minimizing the within-class covariance. In face recognition, linear discriminant analysis (LDA) has been widely adopted owing to its efficiency, but it does not capture nonlinear manifolds of faces which exhibit pose variations. Conventional nonlinear classification methods based on kernels such as generalized discriminant analysis (GDA) and support vector machine (SVM) have been developed to overcome the shortcomings of the linear method, but they have the drawback of high computational cost of classification and overfitting. Our method is for multiclass nonlinear discrimination and it is computationally highly efficient as compared to GDA. The method does not suffer from overfitting by virtue of the linear base structure of the solution. A novel gradient-based learning algorithm is proposed for finding the optimal set of local linear bases. The optimization does not exhibit a local-maxima problem. The transformation functions facilitate robust face recognition in a low-dimensional subspace, under pose variations, using a single model image. The classification results are given for both synthetic and real face data.