Implementation of feature selection and weighting methods for emotion recognition from human actions
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 comp...
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my-unimap-771982022-11-25T01:08:51Z Implementation of feature selection and weighting methods for emotion recognition from human actions Hariharan, Muthusamy, Dr. 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%. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77198 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77198/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77198/1/Page%201-24.pdf 5e5bfa522a54eea576561678cea0052d http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77198/2/Full%20text.pdf 51fa4d0c56ac5e2926a516472c82f16f http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77198/4/Nurnadia.pdf 0c3f9758a0f1db5f9522a123f4524ecf Universiti Malaysia Perlis (UniMAP) Emotion recognition Emotional intelligence Facial expression School of Mechatronic Engineering |
| institution |
Universiti Malaysia Perlis |
| collection |
UniMAP Institutional Repository |
| language |
English |
| advisor |
Hariharan, Muthusamy, Dr. |
| topic |
Emotion recognition Emotional intelligence Facial expression |
| spellingShingle |
Emotion recognition Emotional intelligence Facial expression Implementation of feature selection and weighting methods for emotion recognition from human actions |
| description |
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%. |
| format |
Thesis |
| 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 |
| granting_institution |
Universiti Malaysia Perlis (UniMAP) |
| granting_department |
School of Mechatronic Engineering |
| url |
http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77198/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77198/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77198/4/Nurnadia.pdf |
| _version_ |
1776104248556126208 |