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Preface Chapter 1 Motion Detection with Skeleton Tracking 1.1 Introduction 1.1.1 Fog Computing-based Methods for Physiological Function Assessment 1.1.2 Motion Detection with Skeleton Tracking for Physiological Function Assessment 1.2 Motion Detection Method 1.2.1 Physiological Function Assessment Based on Fog Computing 1.2.2 Motion Recognition with RGB-D Cameras 1.3 Validation for Motion Detection 1.3.1 Comparison with Digital Angle Protractor 1.3.2 Effect of Motion Detection 1.3.3 Effect of Gait Analysis 1.4 3D Stcreo Human Trajectory Learning and Prediction 1.4.1 Method Overview 1.4.2 Twin Deep Neural Networks with Stereo Constraint for 3D Human Pose Estimation 1.4.3 Constructing the Forward and Backward Prediction Networks 1.5 Validation for 3D Pedestrian Trajectory Prediction 1.5.1 3D Pedestrian Trajectory Dataset 1.5.2 Evaluation Metrics and Protocol 1.5.3 Performance Evaluations and Comparison 1.6 Conclusions References Chapter 2 Facial Expression and Emotion Recognition 2.1 Introduction 2.2 Methodologies 2.2.1 Patch Manifold 2.2.2 Patch Discriminative Analysis 2.2.3 Optimization 2.3 Patch Discriminative Analysis Network 2.4 Facial Expression Recognition Validation 2.5 Methodologies Based on LMDAP 2.5.1 Discriminant Embedding Space 2.5.2 Convolutional Kernel Learning Based on the Proposed LMDAP Algorithm 2.5.3 Local Manifold Discriminant Analysis Projections Network 2.6 Validation for Method Based on LMDAP 2.6.1 Datasets 2.6.2 Parameter Setting 2.6.3 Face Recognition Experiments on Extended Yale B Dataset 2.6.4 Face Recognition Experiments on AR Dataset 2.6.5 Face Recognition Experiments on FERET Dataset 2.6.6 Face Verification: YTF 2.6.7 Impact of Parameters 2.6.8 Impact of the Number Block Size 2.6.9 Impact of the Block Overlap Ratio 2.7 Emotion Recognition Based Acoustic Features 2.7.1 MFCC Related Features Extraction 2.7.2 Speech Emotion Recognition Based on CNN-LSTM 2.7.3 Attention-based CNN-BiLSTM Recognition Model (CNN AttBiLSTM) 2.8 Validation for Speech Emotion Recognition 2.8.1 Datasets