Konar A., Chakraborty A. (eds.) Emotion Recognition. A Pattern Analysis Approach

John Wiley, 2015. — 583 p.Emotion represents a psychological state of the human mind. Researchers from different domains have diverse opinions about the developmental process of emotion. Philosophers believe that emotion originates as a result of substantial (positive or negative) changes in our personal situations or environment. Biologists, however, consider our nervous and hormonal systems responsible for the development of emotions. Current research on brain imaging reveals that the cortex and the subcortical region in the frontal brain are responsible for the arousal of emotion. Although there are conflicts in the developmental process of emotion, experimental psychologists reveal that a change in our external or cognitive states carried by neuronal signals triggers our hormonal glands, which in turn excites specific modules in the human brain to develop a feeling of emotion.
The arousal of emotion is usually accompanied with manifestation in our external appearance, such as changes in facial expression, voice, gesture, posture, and other physiological conditions. Recognition of emotion from its external manifestation often leads to inaccurate inferences particularly for two reasons. First, the manifestation may not truly correspond to the arousal of the specific emotion. Second, measurements of external manifestation require instruments of high precision and accuracy. The first problem is unsolvable in case the subjects over which experiments are undertaken suppress their emotion, or pretend to exhibit false emotion. Presuming that the subjects are conducive to the recognition process, we only pay attention to the second problem, which can be solved by advanced instrumentation.
This single volume on Emotion Recognition: A Pattern Analysis Approach provides through and insightful research methodologies on different modalities of emotion recognition, including facial expression, voice, and biopotential signals. It is primarily meant for graduate students and young researchers, who like to initiate their doctoral/MS research in this new discipline. The book is equally useful to professionals engaged in the design/development of intelligent systems for applications in psychotherapy and human–computer interactive systems. It is an edited volume written by several experts with specialized knowledge in the diverse domains of emotion recognition. Naturally, the book contains a thorough and in-depth coverage on all theories and experiments on emotion recognition in a highly comprehensive manner.
The recognition process involves extraction of features from the external manifestation of emotion on facial images, voice, and biopotential signals. All the features extracted are not equally useful for emotion recognition. Thus, the next step to feature extraction is to reduce the dimension of features by feature reduction techniques. The last step of emotion recognition is to employ a classifier or clustering method to classify the measured signals into one specific emotion class. Several techniques of computational intelligence and machine learning can be used here for recognition of emotion from its feature space.Introduction to Emotion Recognition
Exploiting Dynamic Dependencies Among Action Units for Spontaneous Facial Action Recognition
Facial Expressions: A Cross-Cultural Study
A Subject-dependent Facial Expression Recognition System
Facial Expression Recognition Using Independent Component Features and Hidden Markov Model
Feature Selection for Facial Expression based on Rough Set Theory
Emotion Recognition from Facial Expressions Using Type-2 Fuzzy Sets
Emotion Recognition from Non-frontal Facial Images
Maximum a Posteriori based Fusion Method for Speech Emotion Recognition
Emotion Recognition in Naturalistic Speech and Language—A Survey
EEG-Based Emotion Recognition Using Advanced Signal Processing Techniques
Frequency Band Localization on Multiple Physiological Signals for Human Emotion Classification Using DWT
Toward Affective Brain–Computer Interface: Fundamentals and Analysis of EEG-based Emotion Classification
Bodily Expression for Automatic Affect Recognition
Building a Robust System for Multimodal Emotion Recognition
Semantic AudioVisual Data Fusion for Automatic Emotion Recognition
A Multilevel Fusion Approach for Audiovisual Emotion Recognition
From a Discrete Perspective of Emotions to Continuous, Dynamic, and Multimodal Affect Sensing
AudioVisual Emotion Recognition using Semi-Coupled Hidden Markov Model with State-Based Alignment Strategy
Emotion Recognition in Car Industry