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Emotion detection from thermal facial imprint based on GLCM features

Social intelligence in robots has been demonstrated and recognized in numerous contemporary studies especially for Human Robot Interaction (HRI). However, it has become increasingly apparent that social and interactive skills are prerequisites in any application areas and contexts where robots nee...

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Main Authors: Latif, M. H., Md. Yusof, Hazlina, Sidek, Shahrul Na'im, Rusli, Nazreen, Fatai, Sado
Format: Article
Language:English
English
Published: Asian Research Publishing Network (ARPN) 2016
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/49956/1/jeas_0116_3343.pdf
http://irep.iium.edu.my/49956/4/49956_Emotion%20detection%20from%20thermal%20facial%20imprint_scopus.pdf
http://irep.iium.edu.my/49956/
http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_0116_3343.pdf
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spelling my.iium.irep.499562017-10-10T06:24:54Z http://irep.iium.edu.my/49956/ Emotion detection from thermal facial imprint based on GLCM features Latif, M. H. Md. Yusof, Hazlina Sidek, Shahrul Na'im Rusli, Nazreen Fatai, Sado T Technology (General) Social intelligence in robots has been demonstrated and recognized in numerous contemporary studies especially for Human Robot Interaction (HRI). However, it has become increasingly apparent that social and interactive skills are prerequisites in any application areas and contexts where robots need to interact and collaborate with other robots or humans. The main focus now shifted on how the robots should perceive human affective states and manifest it through action. Recognition of human affective states could be achieved through affective computing by using numerous modalities such as speech, facial expression, body language, physiological signals etc. There are two approaches to access the affective states; invasive and noninvasive. Decades of researches and findings were mostly focussed on the invasive approach; Electroencephalogram (EEG), heart rate, blood flow, Galvanic Skin Response (GSR) etc. When it comes to affect recognition using noninvasive approach, very few numbers of publications have been done to date. In this paper, we presented an efficient method for thermal image feature extraction using the Gray Level Co-occurrence Matrix (GLCM) technique. By analysing the heat pattern on the facial skin, this work attempts to investigate the suitability of the thermal imaging technique for affect detection. The findings of this study indicate thermal imaging as a contactless and noninvasive method for appraising human emotional states. Asian Research Publishing Network (ARPN) 2016-01 Article REM application/pdf en http://irep.iium.edu.my/49956/1/jeas_0116_3343.pdf application/pdf en http://irep.iium.edu.my/49956/4/49956_Emotion%20detection%20from%20thermal%20facial%20imprint_scopus.pdf Latif, M. H. and Md. Yusof, Hazlina and Sidek, Shahrul Na'im and Rusli, Nazreen and Fatai, Sado (2016) Emotion detection from thermal facial imprint based on GLCM features. ARPN Journal of Engineering and Applied Sciences, 11 (1). pp. 345-350. ISSN 1819-6608 http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_0116_3343.pdf
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T Technology (General)
spellingShingle T Technology (General)
Latif, M. H.
Md. Yusof, Hazlina
Sidek, Shahrul Na'im
Rusli, Nazreen
Fatai, Sado
Emotion detection from thermal facial imprint based on GLCM features
description Social intelligence in robots has been demonstrated and recognized in numerous contemporary studies especially for Human Robot Interaction (HRI). However, it has become increasingly apparent that social and interactive skills are prerequisites in any application areas and contexts where robots need to interact and collaborate with other robots or humans. The main focus now shifted on how the robots should perceive human affective states and manifest it through action. Recognition of human affective states could be achieved through affective computing by using numerous modalities such as speech, facial expression, body language, physiological signals etc. There are two approaches to access the affective states; invasive and noninvasive. Decades of researches and findings were mostly focussed on the invasive approach; Electroencephalogram (EEG), heart rate, blood flow, Galvanic Skin Response (GSR) etc. When it comes to affect recognition using noninvasive approach, very few numbers of publications have been done to date. In this paper, we presented an efficient method for thermal image feature extraction using the Gray Level Co-occurrence Matrix (GLCM) technique. By analysing the heat pattern on the facial skin, this work attempts to investigate the suitability of the thermal imaging technique for affect detection. The findings of this study indicate thermal imaging as a contactless and noninvasive method for appraising human emotional states.
format Article
author Latif, M. H.
Md. Yusof, Hazlina
Sidek, Shahrul Na'im
Rusli, Nazreen
Fatai, Sado
author_facet Latif, M. H.
Md. Yusof, Hazlina
Sidek, Shahrul Na'im
Rusli, Nazreen
Fatai, Sado
author_sort Latif, M. H.
title Emotion detection from thermal facial imprint based on GLCM features
title_short Emotion detection from thermal facial imprint based on GLCM features
title_full Emotion detection from thermal facial imprint based on GLCM features
title_fullStr Emotion detection from thermal facial imprint based on GLCM features
title_full_unstemmed Emotion detection from thermal facial imprint based on GLCM features
title_sort emotion detection from thermal facial imprint based on glcm features
publisher Asian Research Publishing Network (ARPN)
publishDate 2016
url http://irep.iium.edu.my/49956/1/jeas_0116_3343.pdf
http://irep.iium.edu.my/49956/4/49956_Emotion%20detection%20from%20thermal%20facial%20imprint_scopus.pdf
http://irep.iium.edu.my/49956/
http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_0116_3343.pdf
_version_ 1643613633878425600
score 13.211869

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