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사용자 행동 자세를 이용한 시각계 기반의 감정 인식 연구

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

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참고문헌

키워드

주제분류컴퓨터학

초록

사람의 의도를 인지하기 위해 감정을 시각적으로 인식하는 연구는 전통적으로 감정을 드러내는 얼굴 표정을 인식하는 데 집중해 왔다. 최근에는 감정을 드러내는 신체 언어 즉 신체 행동과 자세를 통해 감정을 나타내는 방법에서 감정 인식의 새로운 가능성을 찾고 있다. 본 연구는신경생리학의 시각계 처리 방법을 적용한 신경모델을 구축하여 행동에서 기본 감정 의도를 인식하는 방법을 제안한다. 이를 위해 시각 피질의정보 처리 모델에 따라 생물학적 체계의 신경모델 검출기를 구축하여 신체 행동의 정적 자세에서 6가지 주요 기본 감정을 판별한다. 파라미터 변화에 강건한 제안 모델의 성능은 신체행동 자세 집합을 대상으로 사람 관측자와의 평가 결과를 비교 평가하여 가능성을 제시한다.

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.

참고문헌 (33)

  1. P. Ekman, "Universal facial expressions of emotion", California Mental Health Research Digest, 8, pp.151~158 (1970)
  2. N. H. Frijda, "The Emotions" (1986), Cambridge University Press
  3. P. Ekman, "Facial expression and emotion", American Psychologist, 48 (1993)
  4. C. Padgett, "Representing face images for emotion classification", Proc. Advances in Neural Information Processing Systems, 통권 : 9 (1996), MIT Press
  5. M. N. Dailey, "EMPATH: a neural network that categorizes facial expressions", Journal of Cognitive Neuroscience, 14(8), pp.1158~1173 (2002)
  6. N. Fragopanagos, "Emotion recognition in human-computer interaction", Neural Networks, 18, pp.389~405 (2005)
  7. P. Ekman, "An argument for basic emotions", Cognition and Emotion, 6(169), pp.384~392 (1992)
  8. M. V. Peelen, "The neural basis of visual body perception", Nature Reviews Neuroscience, 8(8), pp.636~648 (2007)
  9. J. J. Stekelenburg, "The neural correlates of perceiving human bodies: An ERP study on the body-inversion eect", Neuroreport, 15(5), pp.777~780 (2004)
  10. N. Dalal, "Histograms of oriented gradients for human detection", Proc. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 통권 : 1, pp.886~893 (2005)
  11. Z. Zeng, "A survey of affect recognition methods: Audio, visual, and spontaneous expressions", IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(1), pp.39~48 (2009)
  12. T. Serre, "Robust object recognition with cortex-like mechanisms", IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3), pp.411~426 (2006)
  13. A. J. Calder, "Categorical perception of morphed facial expressions", Visual Cognition, 361, pp.81~117 (1996)
  14. B. de Gelder, "Categorical perception of facial expressions: categories and their internal structure", Cognition and Emotion, 11(1), pp.1~23 (1997)
  15. B. de Gelder, "Towards the neurobiology of emotional body language", Nature Reviews Neuroscience, 7(3), pp.242~249 (2006)
  16. H. K. M. Meeren, "Rapid perceptual integration of facial expression and emotional body language", Proc. National Academy of Sciences of the USA, 102(45), pp.16518~16523 (2005)
  17. D. J. Felleman, "Distributed hierarchical processing in the primate visual cortex", Cerebral Cortex, 1, pp.1~47 (1991)
  18. M. Riesenhuber, "Hierarchical model of object recognition in cortex", Nature Neuroscience, 20, pp.1019~1025 (1999)
  19. 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, 4(12), pp.2379~2394 (1987)
  20. J. Hedge, "Selectivity for complex shapes in primate visual area V2", Journal of Neuroscience, 20(RC61), pp.1~6 (2000)
  21. K. Fukushima, "Neocognitron: a self-organizing neural network model for mechanisms of pattern recognition unacted by shift in position", Biological Cybernetics, 36, pp.193~202 (1980)
  22. T. J. Gawne, "Response of primate visual cortical V4 neurons to two simultaneously presented stimuli", Journal of Neurophysiology, 88(17), pp.1128~1135
  23. I. Lampl, "Intracellular measurements of spatial integration and the max operation in complex cells of the cat primary visual cortex", Journal of Neurophysiology, 9292, pp.2704~2713 (2004)
  24. N. K. Logothetis, "Shape representation in the inferior temporal cortex of monkeys", Current Biology, 5, pp.552~563 (1995)
  25. D. Anguita, "Improved neural network for SVM learning", IEEE Transactions on Neural Networks, 13, pp.1243~1244 (2002)
  26. M. A. Giese, "Neural mechanisms for the recognition of biological movements", Nature Neuroscience, 4, pp.179~192 (2003)
  27. J. A. Beintema, "Perception of biological motion without local image motion", Proc. National Academy of Sciences of the USA, 통권 : 99, pp.5661~5663 (2002)
  28. P. Downing, "A cortical area selective for visual processing of the human body", Science, 293, pp.2470~2473 (2001)
  29. N. Sebe, "Towards authentic emotion recognition", Proc. International Conference on Systems, Man, and Cybernetics (2004)
  30. 김진옥, "상황에 민감한 베이지안 분류기를 이용한 얼굴 표정 기반의 감정 인식", 정보처리학회논문지. 소프트웨어 및 데이터 공학, 13(7), pp.653~662 (2006), 한국정보처리학회
  31. B. Boulay, "Applying 3D human model in a posture recognition system", Pattern Recognition Letters, 27(15), pp.1788~1796 (2006)
  32. M. W. Lee, "A model-based approach for estimating human 3D poses in static images", IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6), pp.905~916 (2006)
  33. S. Ali, "Human action recognition in videos using kinematic features and multiple instance learning", IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(2), pp.288~303 (2010)

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