Emotion detection using relative grid based coefficients through human facial expressions

Facial expressions are important in human computer interaction, because the machine can thereby understand human reactions and act accordingly. Facial expressions act like a nonverbal communication cues in human-human or human computer interactions. In noises environment, getting visual data is diff...

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Main Authors: Kudiri, K.M., Said, A.M., Nayan, M.Y.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.32502 /
Published: 2013
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897827071&doi=10.1109%2fICRIIS.2013.6716683&partnerID=40&md5=acada4914991183309fbe41e27c4541d
http://eprints.utp.edu.my/32502/
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spelling utp-eprints.325022022-03-29T14:04:21Z Emotion detection using relative grid based coefficients through human facial expressions Kudiri, K.M. Said, A.M. Nayan, M.Y. Facial expressions are important in human computer interaction, because the machine can thereby understand human reactions and act accordingly. Facial expressions act like a nonverbal communication cues in human-human or human computer interactions. In noises environment, getting visual data is difficult. According to the relative bin sub-image based studies, high dimensionality is affecting the system which consequently affects the performance of the emotion detection system. Due to these reasons, a new approach using relative grid coefficient feature extraction through visual data is proposed. Support vector machine with radial basis kernel is used for the classification of emotions. Preliminary results showed that an average of 89 accuracy was obtained for relative grid based features. © 2013 IEEE. 2013 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897827071&doi=10.1109%2fICRIIS.2013.6716683&partnerID=40&md5=acada4914991183309fbe41e27c4541d Kudiri, K.M. and Said, A.M. and Nayan, M.Y. (2013) Emotion detection using relative grid based coefficients through human facial expressions. In: UNSPECIFIED. http://eprints.utp.edu.my/32502/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Facial expressions are important in human computer interaction, because the machine can thereby understand human reactions and act accordingly. Facial expressions act like a nonverbal communication cues in human-human or human computer interactions. In noises environment, getting visual data is difficult. According to the relative bin sub-image based studies, high dimensionality is affecting the system which consequently affects the performance of the emotion detection system. Due to these reasons, a new approach using relative grid coefficient feature extraction through visual data is proposed. Support vector machine with radial basis kernel is used for the classification of emotions. Preliminary results showed that an average of 89 accuracy was obtained for relative grid based features. © 2013 IEEE.
format Conference or Workshop Item
author Kudiri, K.M.
Said, A.M.
Nayan, M.Y.
spellingShingle Kudiri, K.M.
Said, A.M.
Nayan, M.Y.
Emotion detection using relative grid based coefficients through human facial expressions
author_sort Kudiri, K.M.
title Emotion detection using relative grid based coefficients through human facial expressions
title_short Emotion detection using relative grid based coefficients through human facial expressions
title_full Emotion detection using relative grid based coefficients through human facial expressions
title_fullStr Emotion detection using relative grid based coefficients through human facial expressions
title_full_unstemmed Emotion detection using relative grid based coefficients through human facial expressions
title_sort emotion detection using relative grid based coefficients through human facial expressions
publishDate 2013
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897827071&doi=10.1109%2fICRIIS.2013.6716683&partnerID=40&md5=acada4914991183309fbe41e27c4541d
http://eprints.utp.edu.my/32502/
_version_ 1741197742497071104
score 11.62408

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