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Table of contents
List of figures
List of tables
1 Introduction
1.1 What can be inferred from faces?
1.1.1 Facial Action Coding System
1.1.2 Towards high level information
1.2 Automatic facial analysis: applications and challenges
1.2.1 A few applications
1.2.2 How do automatic facial analysis systems work?
1.2.3 Difficulties in automatic facial expression analysis
1.2.4 International challenges
1.3 From landmark detection towards emotion recognition
1.3.1 Landmark detection
1.3.2 AU prediction
1.3.3 Mental state recognition
1.4 Outline and contributions
2 Hard Multi-Task Metric Learning for Kernel Regression
2.1 Introduction to machine learning
2.1.1 A few definitions
2.1.2 Different methods
2.1.3 Understanding overfitting
2.2 Different model types
2.2.1 Non-parametric models
2.2.2 Parametric models
2.2.3 Semi-parametric models
2.3 Metric Learning for Kernel Regression
2.3.1 The MLKR method
2.3.2 About MLKR convexity
2.3.3 About MLKR complexity
2.3.4 About overfitting
2.3.5 About Nadaraya-Watson extrapolation capabilities
2.4 Our extensions
2.4.1 Feature selection
2.4.2 Stochastic gradient descent
2.4.3 Lasso-regularization
2.4.4 Multi-dimensional label extensions
2.5 Conclusion
3 Facial landmark detection
3.1 Introduction
3.2 Commonly used appearance features
3.3 The 300W database
3.4 Our facial landmark prediction framework
3.4.1 Feature extraction
3.4.2 Proposed regression method
3.4.3 Experimental setup
3.5 Results on the 300W dataset
3.5.1 HOG normalizations
3.5.2 Comparison to CS-MLKR
3.5.3 Embedding more training data samples
3.5.4 Comparison to global PCA and Linear Regression
3.5.5 Comparison to state-of-the-art methods
3.6 Conclusion
4 Action Unit prediction
4.1 Introduction
4.2 The BP4D dataset
4.3 AU prediction framework
4.3.1 Feature extraction
4.3.2 About learning with video data
4.3.3 Experimental setup
4.4 Results on the BP4D dataset
4.4.1 Analysis of feature impact
4.4.2 Evaluations and results on the BP4D dataset
4.4.3 Evaluation of regularization impact
4.4.4 Comparison to baseline systems on the FERA’15 development set .
4.4.5 Comparison to baseline systems on the FERA’15 test set
4.4.6 Comparison to other participants on the FERA’15 test set
4.5 Conclusion
5 Conclusion and future works
5.1 Conclusion
5.2 Future works
5.2.1 Towards coupling database design and model training
5.2.2 Towards smart system adaptation
5.2.3 Towards handling big data sets
References
Appendix A Iterative Regularized Metric Learning
Appendix B Emotion Prediction in a Continuous Space
Appendix C Binary Map based Landmark Localization
