A timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals
This book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers.
Written by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability.
There is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems.
Emotion Recognition: A Pattern Analysis Approach would be of great interest to researchers, graduate students and practitioners, as the book
- 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: 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), Neural Networks, Neural Networks