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Implementation of feature selection and weighting methods for emotion recognition from human actions

Doctor of Philosophy in Biomedical Electronic Engineering

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Bibliographic Details
Main Author: Nurnadia, Mohamad Khair
Other Authors: Hariharan, Muthusamy, Dr.
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2016
Subjects:
Emotion recognition
Emotional intelligence
Facial expression
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77198
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spelling my.unimap-771982022-11-25T01:08:51Z Implementation of feature selection and weighting methods for emotion recognition from human actions Nurnadia, Mohamad Khair Hariharan, Muthusamy, Dr. Emotion recognition Emotional intelligence Facial expression Doctor of Philosophy in Biomedical Electronic Engineering Emotion is a natural, instinctive state of mind emanating from one's circumstances, mood or relationships with others. Emotion can be characterized primarily by the psycho-physiological expressions, biological reactions, body interaction and mental states. In social interaction, emotional component serves as an important element in communication, response and conveying information. Every day, the human body has evolved to perform sophisticated tasks to carry information about emotions. Recent years have seen a significant expansion in research on computational models of human emotional processes primarily in body interaction. However, most researchers fail to address the problem in preprocessing technologies and mainly rely on traditional methods to interpret emotion. Thus, this thesis aims to develop improved emotion recognition methods from human actions which includes the extraction of dynamic descriptors (distance, speed, magnitude of acceleration and magnitude of jerk) and statistical features (mean, maximum, minimum, standard deviation, median, log-energy, RMS and entropy) from joint position data, feature selection/reduction (Relief-F, fast correlation-based filter (FCBF), correlation feature selection (CFS), linear discriminant analysis (LDA) and principle component analysis (PCA), feature weighting methods (feature weighting based on fuzzy Cmean (FWFCM) and feature weighting based on binary encoded output (FWBEO)) and recognition of emotions using different classifiers (K-nearest neighbor (KNN), fuzzy K-nearest neighbor (FKNN) and probabilistic neural network (PNN)). After feature extraction, irrelevant/redundant features were removed using Relief-F, FCBF, CFS, LDA and PCA. Further, to reduce the higher degree of overlap among the relevant/non-redundant features, this thesis proposes FWFCM and FWBEO based feature weighting methods to enhance the discrimination ability of the features and also to minimize the degree of overlap among the features. Different emotion recognition experiments such as subject dependent, subject independent, gender dependent and gender independent were carried out. The performance measures such as overall accuracy and g-mean were considered for the evaluation of the classifiers. The experimental results demonstrate that the proposed feature weighting methods (FWFCM and FWBEO) are effective to classify emotion in human action with a maximum accuracy of 100%. 2016 2022-11-25T01:05:09Z 2022-11-25T01:05:09Z Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77198 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) School of Mechatronic Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Emotion recognition
Emotional intelligence
Facial expression
spellingShingle Emotion recognition
Emotional intelligence
Facial expression
Nurnadia, Mohamad Khair
Implementation of feature selection and weighting methods for emotion recognition from human actions
description Doctor of Philosophy in Biomedical Electronic Engineering
author2 Hariharan, Muthusamy, Dr.
author_facet Hariharan, Muthusamy, Dr.
Nurnadia, Mohamad Khair
format Thesis
author Nurnadia, Mohamad Khair
author_sort Nurnadia, Mohamad Khair
title Implementation of feature selection and weighting methods for emotion recognition from human actions
title_short Implementation of feature selection and weighting methods for emotion recognition from human actions
title_full Implementation of feature selection and weighting methods for emotion recognition from human actions
title_fullStr Implementation of feature selection and weighting methods for emotion recognition from human actions
title_full_unstemmed Implementation of feature selection and weighting methods for emotion recognition from human actions
title_sort implementation of feature selection and weighting methods for emotion recognition from human actions
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2016
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77198
_version_ 1753972993649803264
score 13.222552

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