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http://dx.doi.org/10.3745/KIPSTB.2011.18B.5.305

A Study on Visual Perception based Emotion Recognition using Body-Activity Posture  

Kim, Jin-Ok (대구한의대학교 국제문화정보대학 모바일콘텐츠학부)
Abstract
Research into the visual perception of human emotion to recognize an intention has traditionally focused on emotions of facial expression. Recently researchers have turned to the more challenging field of emotional expressions through body posture or activity. Proposed work approaches recognition of basic emotional categories from body postures using neural model applied visual perception of neurophysiology. In keeping with information processing models of the visual cortex, this work constructs a biologically plausible hierarchy of neural detectors, which can discriminate 6 basic emotional states from static views of associated body postures of activity. The proposed model, which is tolerant to parameter variations, presents its possibility by evaluating against human test subjects on a set of body postures of activities.
Keywords
Activity Posture Recognition; Emotion Recognition; Visual Perception;
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1 J. A. Beintema, M. Lappe, Perception of biological motion without local image motion, Proc. National Academy of Sciences of the USA, Vol.99, pp. 5661-5663, 2002.   DOI   ScienceOn
2 P. Downing, Y. Jiang, M. Shuman, N. A. Kanwisher, A cortical area selective for visual processing of the human body, Science, Vol.293, pp.2470-2473, 2001.   DOI   ScienceOn
3 N. Sebe, Y. Sun, E. Bakker, M. Lew, I. Cohen, T. Huang, Towards authentic emotion recognition, Proc. International Conference on Systems, Man, and Cybernetics, 2004.
4 김진옥, 상황에 민감한 베이지안분류기를 이용한 얼굴표정 기 반의 감정인식, 한국정보처리학회논문지, 13-B권, pp. 653-662, 2006.   과학기술학회마을   DOI   ScienceOn
5 B. Boulay, F. Bremond, M. Thonnat, Applying 3D human model in a posture recognition system, Pattern Recognition Letters, Vol.27, No.15, pp.1788-1796, 2006.   DOI   ScienceOn
6 M. W. Lee, I. Cohen, A model-based approach for estimating human 3D poses in static images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.28, No.6, pp.905-916, 2006.   DOI   ScienceOn
7 S. Ali, M. Shah, Human action recognition in videos using kinematic features and multiple instance learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.32, No.2, pp.288-303, 2010.   DOI   ScienceOn
8 D. J. Felleman, D. C. van Essen, Distributed hierarchical processing in the primate visual cortex, Cerebral Cortex, Vol.1, pp.1-47, 1991.
9 M. Riesenhuber, T. Poggio. Hierarchical model of object recognition in cortex, Nature Neuroscience, Vol.2, pp.1019-1025, 1999.   DOI   ScienceOn
10 D. J. Field, Relations between the statistics of natural images and the response properties of cortical cells, Journal of The Optical Society of America A, Vol.4, No.12, pp.2379-2394, 1987.   DOI
11 J. Hedge, D. C. van Essen, Selectivity for complex shapes in primate visual area V2, Journal of Neuroscience, Vol. 20(RC61), pp.1-6, 2000.
12 K. Fukushima, Neocognitron: a self-organizing neural network model for mechanisms of pattern recognition unacted by shift in position, Biological Cybernetics, Vol.36, pp.193-202, 1980.   DOI
13 T. J. Gawne, J. Martin, Response of primate visual cortical V4 neurons to two simultaneously presented stimuli, Journal of Neurophysiology, Vol.88, No.17, pp.1128-1135. 2002.   DOI
14 I. Lampl, D. Ferster, T. Poggio, M. Riesenhuber, Intracellular measurements of spatial integration and the max operation in complex cells of the cat primary visual cortex, Journal of Neurophysiology, Vol.9292, pp.2704-2713, 2004.
15 N. K. Logothetis, J. Pauls, T. Poggio, Shape representation in the inferior temporal cortex of monkeys, Current Biology, Vol.5, pp.552-563, 1995.   DOI   ScienceOn
16 D. Anguita, A. Boni, Improved neural network for SVM learning, IEEE Transactions on Neural Networks, Vol.13, pp.1243-1244, 2002.   DOI   ScienceOn
17 M. A. Giese, T. Poggio, Neural mechanisms for the recognition of biological movements, Nature Neuroscience, Vol.4, pp.179-192, 2003.   DOI   ScienceOn
18 P. Ekman, An argument for basic emotions, Cognition and Emotion, Vol.6, 169, pp.384-392, 1992.
19 J. J. Stekelenburg, B. de Gelder, The neural correlates of perceiving human bodies: An ERP study on the body-inversion eect, Neuroreport, Vol.15 , No.5, pp.777-780, 2004.   DOI   ScienceOn
20 M. V. Peelen, P. E. Downing, The neural basis of visual body perception, Nature Reviews Neuroscience, Vol.8, No.8, pp.636-648, 2007.   DOI   ScienceOn
21 N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, Proc. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.1, pp.886-893, 2005.
22 Z. Zeng, M. Pantic, G. Roisman, T. Huang, A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.31, No.1, pp,39-48, 2009.   DOI   ScienceOn
23 T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, T. Poggio, Robust object recognition with cortex-like mechanisms, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.29, No.3, pp.411-426, 2006.
24 A. J. Calder, A. W. Young, D. I. Perrett, N. L. Etco, D. Rowland, Categorical perception of morphed facial expressions, Visual Cognition, Vol.3, pp.81-117, 1996.   DOI
25 B. de Gelder, J. P. Teunisse, P. J. Benson, Categorical perception of facial expressions: categories and their internal structure, Cognition and Emotion, Vol.11, No.1, pp.1-23, 1997.   DOI   ScienceOn
26 B. de Gelder, Towards the neurobiology of emotional body language, Nature Reviews Neuroscience, Vol.7, No.3, pp.242-249, 2006.   DOI   ScienceOn
27 H. K. M. Meeren, C. van Heijnsbergen, B. de Gelder, Rapid perceptual integration of facial expression and emotional body language, Proc. National Academy of Sciences of the USA, Vol.102, No.45, pp.16518-16523, 2005.   DOI   ScienceOn
28 P. Ekman, Facial expression and emotion, American Psychologist, Vol.48, 1993.
29 P. Ekman, Universal facial expressions of emotion, California Mental Health Research Digest, Vol.8, pp.151-158, 1970.
30 N. H. Frijda, The Emotions, Cambridge University Press, 1986.
31 C. Padgett, G. W. Cottrell, Representing face images for emotion classification, Proc. Advances in Neural Information Processing Systems, Vol.9, MIT Press, 1996.
32 M. N. Dailey, G. W. Cotrell, C. Padgett, R. Adolphs, EMPATH: a neural network that categorizes facial expressions, Journal of Cognitive Neuroscience, Vol.14, No.8, pp.1158-1173, 2002.   DOI   ScienceOn
33 N. Fragopanagos, J. G. Taylor, Emotion recognition in human-computer interaction, Neural Networks, Vol.18, pp.389-405, 2005.   DOI   ScienceOn