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Thermal Imaging-Based Human Emotion Detection: GLCM Feature Extraction Approach

Abd Latif, M. Yusof, S. Sidek, N. Rusli


Recent studies in artificial intelligence and robotics proved the ability of social intelligence in robots. However, it has become increasingly apparent that social and interactive skills are necessary requirements in any application areas and contexts where robots is needed to interact and collaborate with other robots or humans. In order to develop an emotionally intelligent robot, the issues on how to perceive human emotion (affective) states and how to manifest the robot’s emotion should be addressed. 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 attempt to investigate the suitability of the thermal imaging technique for affect detection. The findings of this study indicate thermal imaging as an alternative, contactless and noninvasive method for appraising human emotional states.

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W. R. Picard, "Affective computing: challenges," International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction, vol. 59, pp. 55-64, 2003.

B. R. Nhan and T. Chau, “Classifying Affective States Using Thermal Infrared Imaging of the Human Face,” IEEE Trans. Biomedical Eng., vol. 57,no. 4, pp. 979-987, April 2010.

A. Merla and G.L. Romani, “Thermal signature of emotional arousal: a functional infrared imaging study,” Proceedings of the 29th Annual International Conference of the IEEE EMBS Cite Internationale, Lyon, France, August 23-26, 2007.

L. Zhilei and W. Shangfei.”Emotion Recognition using Hidden Markov Models from Facial Temperature Sequences,” Springer-Verlag Berlin Heidelberg, pp. 240-247, 2011.

Yoshitomi, Y. “Facial Expression Recognition for Speaker Using Thermal Image Processing and Speech Recognition System,” in Proc. of 10th WSEAS International Conference on Applied Computer Science,

pp. 182–186, 2010.

B. Hern´andez,G. Olague, R. Hammoud, L. Trujillo,E. Romero.” Visual

learning of texture descriptors for facial expression recognition in

thermal imagery.” in Computer Vision and Image Understanding 106(2-

, pp. 258–269, 2007.

M.M. Khan, R.D. Ward and M. Ingleby . “Classifying pretended and

evoked facial expressions of positive and negative affective states using

infrared measurement of skin temperature,” Trans. Appl. Percept. 6, 1,

T. Bourlai, A.Ross, C.Chen and L.Hornak ” A Study on Using Mid-

Wave Infrared Images for Face Recognition,” in Sensing Technologies

for Global Health, Military Medicine, Disaster Response, and

Environmental Monitoring II; and Biometric Technology for Human

Identification IX, Proc. of SPIE Vol. 8371, 2012.

D. Shastri, A.Merla, P. Tsiamyrtzis, and I. Pavlidis, “Imaging facial

signs of neurophysiological responses,” IEEE Trans. Biomed. Eng., vol.

, no. 2, pp. 477–484, Feb. 2009.

I. Pavlidis, J. Dowdall, N. Sun, C. Puri, J. Fei, andM. Garbey,

“Interacting with human physiology,” Comput. Vis. Image

Understanding, vol. 108, no. 1–2, pp. 150–170, 2007.

I. Pavlidis and J. Levine, “Thermal image analysis for polygraph

testing,” IEEE Engineering in Medicine and Biology Magazine,

(6):56 – 64, 2002.

Z. Zhu, P. Tsiamyrtzis and I.Pavlidis, “Forehead thermal signature

extraction in Lie Detection,”in Proceeding of the 29th Annual

International Conference of the IEEE EMBS Cite Internationale, Lyon,

France, August 23-26, pp.243-246, 2007.



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