Thermal Imaging-Based Human Emotion Detection: GLCM Feature Extraction Approach
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
M.Berking & B. Whitley, “Emotion Regulation: Definition and Relevance for Mental Health”, in M. Berking & B. Whitley (Eds.), Affect Regulation Training: A Practitioners’ Manual. New York: Springer, pp. 5-17, 2014.
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(2-3), 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, 2009.
K.Krauchi & A. Wirz-Justice, “Circadian rhythm of heat production, heart rate and skin and core temperature under unmasking condition”, in men. American Journal of Physiology, 267 (3), pp. 819-829, 1994.
M. Murugappan & S. Murugappan, “Human emotion recognition through short time Electroencephalogram (EEG) signals using Fast Fourier Transform (FFT)”, in Signal Processing and its Applications (CSPA), 2013 IEEE 9th International Colloquium, 2013.
Murugappan, M., & Murugappan, S. (2013). Human emotion recognition through short time Electroencephalogram (EEG) signals using Fast Fourier Transform (FFT). Paper presented at the Signal Processing and its Applications (CSPA), 2013 IEEE 9th International Colloquium.
M.Wollmer,F.Weninger,T. Knaup, B. Schuller, C. Sun, K. Sagae, & L-P. Morency,”YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context”, in Intelligent Systems, IEEE, 28(3), pp. 46-53, 2013.
D. Shastri, A.Merla, P. Tsiamyrtzis, and I. Pavlidis, “Imaging facial signs of neurophysiological responses,” IEEE Trans. Biomed. Eng., vol. 56, 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, 21(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.
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.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.