**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.
