Kernel Learning Algorithms for Face Recognition

Description

Preface Acknowledgements Contents 1 Introduction 1.1…Basic Concept 1.1.1 Supervised LearningSupervised Learning 1.1.2 Unsupervised LearningUnsupervised learning 1.1.3 Semi-Supervised Algorithms 1.2…Kernel LearningKernel learning 1.2.1 Kernel Definition 1.2.2 Kernel Character 1.3…Current Research Status 1.3.1 Kernel ClassificationKernel classification 1.3.2 Kernel ClusteringKernel Clustering 1.3.3 Kernel Feature ExtractionKernel feature extraction 1.3.4 Kernel Neural NetworkKernel neural network 1.3.5 Kernel Application 1.4…Problems and Contributions 1.5…Contents of this Book References 2 Statistical Learning-Based Face Recognition 2.1…Introduction 2.2…Face Recognition: Sensory Inputs 2.2.1 Image-Based Face Recognition 2.2.2 Video-Based Face Recognitionface recognition 2.2.3 3D-Based Face Recognitionface recognition 2.2.4 Hyperspectral Image-Based Face Recognitionface recognition 2.3…Face RecognitionFace recognition: Methods 2.3.1 Signal Processing-Based Face Recognition 2.3.2 A Single Training Image per Person Algorithm 2.4…Statistical Learning-Based Face Recognitionface recognition 2.4.1 Manifold LearningManifold learning-Based Face Recognitionface recognition 2.4.2 Kernel LearningKernel learning-Based Face Recognition 2.5…Face RecognitionFace recognition: Application Conditions References 3 Kernel Learning Foundation 3.1…Introduction 3.2…Linear Discrimination and Support Vector MachineSupport Vector Machine 3.3…Kernel LearningKernel learning: Concepts 3.4…Kernel LearningKernel learning: Methods 3.4.1 Kernel-Based HMMs 3.4.2 Kernel-Independent Component Analysis 3.5…Kernel-Based Online SVR 3.6…Optimized Kernel-Based Online SVR 3.6.1 Method I: Kernel-Combined Online SVR 3.6.2 Method II: Local Online Support Vector Regression 3.6.3 Method III: Accelerated Decremental Fast Online SVR 3.6.4 Method IV: Serial Segmental Online SVR 3.6.5 Method V: Multi-scale Parallel Online SVR 3.7…Discussion on Optimized Kernel-Based Online SVR 3.7.1 Analysis and Comparison of Five Optimized Online SVR Algorithms 3.7.2 Application Example References 4 Kernel Principal Component Analysis (KPCA)-Based Face Recognition 4.1…Introduction 4.2…Kernel Principal Component AnalysisKernel Principal Component Analysis 4.2.1 Principal Component Analysis 4.2.2 Kernel Discriminant Analysis 4.2.3 Analysis on KPCAKPCA and KDA 4.3…Related Improved KPCAKPCA 4.3.1 Kernel Symmetrical Principal Component Analysis 4.3.2 Iterative Kernel Principal Component AnalysisKernel Principal Component Analysis 4.4…Adaptive Sparse Kernel Principal Component AnalysisSparse Kernel Principal Component Analysis 4.4.1 Reducing the Training Samples with Sparse Analysis 4.4.2 Solving the Optimal Projection Matrix 4.4.3 Optimizing Kernel Structure with the Reduced Training Samples 4.4.4 Algorithm Procedure 4.5…Discriminant Parallel KPCA-Based Feature Fusion 4.5.1 Motivation 4.5.2 Method 4.6…Three-Dimensional Parameter Selection PCA-Based Face Recognition 4.6.1 Motivation 4.6.2 Method 4.7…Experiments and Discussion 4.7.1 Performance on KPCAKPCA and Improved KPCA on UCI Dataset 4.7.2 Performance on KPCAKPCA and Improved KPCA on ORL Database 4.7.3 Performance on KPCAKPCA and Improved KPCA on Yale Database 4.7.4 Performance on Discriminant Parallel KPCA-Based Feature Fusion 4.7.5 Performance on Three-Dimensional Parameter Selection PCA-Based Face Recognitionface recognition 5 Kernel Discriminant Analysis Based Face Recognition 5.1…Introduction 5.2…Kernel Discriminant Analysis 5.3…Adaptive Quasiconformal Kernel Discriminant AnalysisKernel Discriminant Analysis 5.4…Common Kernel Discriminant AnalysisCommon Kernel Discriminant Analysis 5.4.1 Kernel Discriminant Common Vector Analysis with Space Isomorphic Mapping 5.4.2 Gabor Feature Analysis 5.4.3 Algorithm Procedure 5.5…Complete Kernel Fisher Discriminant Analysis 5.5.1 Motivation 5.5.2 Method 5.6…Nonparametric Kernel Discriminant AnalysisNonparametric Kernel Discriminant Analysis 5.6.1 Motivation 5.6.2 Method 5.7…Experiments on Face RecognitionFace Recognition 5.7.1 Experimental Setting 5.7.2 Experimental Results of AQKDA 5.7.3 Experimental Results of Common Kernel Discriminant AnalysisCommon Kernel Discriminant Analysis 5.7.4 Experimental Results of CKFD 5.7.5 Experimental Results of NKDA References 6 Kernel Manifold Learning-Based Face Recognition 6.1…Introduction 6.2…Locality Preserving Projection 6.3…Class-Wise Locality Preserving ProjectionClass-wise Locality Preserving Projection 6.4…Kernel Class-Wise Locality Preserving ProjectionKernel Class-wise Locality Preserving Projection 6.5…Kernel Self-Optimized Locality Preserving Discriminant AnalysisKernel Self-Optimized Locality Preserving Discriminant Analysis 6.5.1 Outline of KSLPDA 6.6…Experiments and Discussion 6.6.1 Experimental Setting 6.6.2 Procedural Parameters 6.6.3 Performance Evaluation of KCLPP 6.6.4 Performance Evaluation of KSLPDA References 7 Kernel Semi-Supervised Learning-Based Face Recognition 7.1…Introduction 7.2…Semi-Supervised Graph-Based Global and Local Preserving Projection 7.2.1 Side-Information-Based Intrinsic and Cost Graph 7.2.2 Side-Information and k-Nearest Neighbor-Based Intrinsic and Cost Graph 7.2.3 Algorithm Procedure 7.2.4 Simulation Results 7.3…Semi-Supervised Kernel LearningSemi-Supervised Kernel Learning 7.3.1 Ksgglpp 7.3.2 Experimental Results References 8 Kernel-Learning-Based Face Recognition for Smart Environment 8.1…Introduction 8.2…Framework 8.3…Computation 8.3.1 Feature ExtractionFeature extraction Module 8.3.2 Classification 8.4…Simulation and Analysis 8.4.1 Experimental Setting 8.4.2 Results on Single Sensor Data 8.4.3 Results on Multisensor Data References 9 Kernel-Optimization-Based Face Recognition 9.1…Introduction 9.2…Data-Dependent KernelData-dependent Kernel Self-optimization 9.2.1 Motivation and Framework 9.2.2 Extended Data-Dependent Kernel 9.2.3 Kernel Optimization 9.3…Simulations and Discussion 9.3.1 Experimental Setting and Databases 9.3.2 Performance Evaluation on Two Criterions and Four Definitions of e\left( {x,\,z_{n} } \right) 9.3.3 Comprehensive Evaluations on UCI Dataset 9.3.4 Comprehensive Evaluations on Yale and ORL Databases 9.4…Discussion References 10 Kernel Construction for Face Recognition 10.1…Introduction 10.2…Matrix Norm-Based Gaussian Kernel 10.2.1 Data-Dependent Kernel 10.2.2 Matrix Norm-Based Gaussian Kernel 10.3…Adaptive Matrix-Based Gaussian Kernel 10.3.1 Theory Deviation 10.3.2 Algorithm Procedure 10.4…Experimental Results 10.4.1 Experimental Setting 10.4.2 Results References Index