- Offers both foundations and advances on emotion recognition in a single volume
- Provides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains
- Inspires young researchers to prepare themselves for their own research
- Demonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.
Keywords: Pattern Analysis, Emotion recognition
Emotion recognition, facial expression, bio-potential signals, voice-potential signals, pulse rate, body temperature, neuro-fuzzy techniques, support vector machine (SVM), reinforcement learning, principal component analysis, hidden Markov model, probabilistic models, feature extraction, feature reduction, classification of emotions, human-computer interface design, feature selection, classifier design, multi-modal fusion, facial action, Dynamics Bayesian Network (DBN) model, Action Units, Radial Basis Function (RBF), Fisher Linear Discriminant Analysis (FLDA), GT2FS, IT2FS, Local Binary Pattern (LBP), Gabor Wavelet features, probabilistic neural net (KNN), universal background model-Gaussian mixture model (UBM-GMM), Multi-Dimensional Directed Information Analysis, electromyogram (EMG), electrocardiogram (ECG), Butterworth filter, semantic audio-visual data fusion, HMMs, Local Binary Patterns (LBPs), mel-frequency cepstral Coefficients (MFCCs), Sector Volumetric Differences Feature/Volumetric Differences Feature (SVDF/VDF), Semi Coupled Hidden Markov Model (SC-HMM)