Liyu Gong
ABSTRACT
In this paper, we address the problem of recognizing affect from non-stylized human body motion. We utilize a novel feature descriptor which is based on the shape of signal probability density function framework to represent the motion capture data. Combining the feature representation scheme with support vector machine classifier, we detect implicitly communicated affect in human body motion. We test our algorithm using a comprehensive database of affectively performed motion. Experiment results show state-of-the-art performance compared with the existing methods.
- D. Bernhardt and P. Robinson. Detecting affect from non-stylised body motions. In International Conference on Affective Computing and Intelligent Interaction (ACII), 2007. Google Scholar
Digital Library
- N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005. Google ScholarDigital Library
- L. Gong, T. Wang, and F. Liu. Shape of gaussians as feature descriptors. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.Google Scholar
- L. Gong, T. Wang, Y. Yu, F. Liu, and X. Hu. A lie group based gaussian mixture model distance measure for multimedia comparison. In International Conference on Internet Multimedia Computing and Service, 2009. Google ScholarDigital Library
- H. Gunes and M. Piccardi. Automatic temporal segment detection and affect recognition from face and body display. IEEE Transaction on System, Man and Cybernetics, 39(1):64--84, 2009. Google ScholarDigital Library
- A. Kapur, A. Kapur, N. Virji-Babul, G. Tzanetakis, and P. F. Driessen. Gesture-based affective computing on motion capture data. In International Conference on Affective Computing and Intelligent Interaction (ACII), 2005. Google ScholarDigital Library
- Y. Ma, H. M. Paterson, and F. E. Pollick. A motion capture library for the study of identity, gender, and emotion perception from biological motion. Behavior Research Methods, 38(1):134--141, 2006.Google ScholarCross Ref
- M. Pantic, N. Sebe, J. F. Cohn, and T. Huang. Affective multimodal human-computer interaction. In ACM International Conference on Multimedia (MM), 2005. Google ScholarDigital Library
- R. W. Picard. Affective Computing. The MIT Press, Cambridge, MA, USA, 1997. Google ScholarDigital Library
- O. Tuzel, F. Porikli, and P. Meer. Region covariance: A fast descriptor for detection and classification. In Eurpean Conference on Computer Vision (ECCV), 2006. Google ScholarDigital Library
- O. Tuzel, F. Porikli, and P. Meer. Pedestrian detection via classification on riemannian manifolds. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 30(10):1713--1727, 2008. Google ScholarDigital Library
- P. Yang, Q. Liu, and D. N. Metaxas. Boosting coded dynamic features for facial action units and facial expression recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007.Google ScholarCross Ref
- Z. Zeng, M. Pantic, G. I. Roisman, and T. S. Huang. A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 30(1):38--58, 2009. Google ScholarDigital Library
- Z. Zeng, J. Tu, M. Liu, T. S. Huang, B. Pianfetti, D. Roth, and S. Levinson. Audio-visual affect recognition. IEEE Transaction on Multimedia, 9(2):424--428, 2007. Google ScholarDigital Library
Index Terms
- Recognizing affect from non-stylized body motion using shape of Gaussian descriptors
Recommendations
Emotion Recognition Using Physiological Signals
MIDI '15: Proceedings of the Mulitimedia, Interaction, Design and InnnovationIn this paper the problem of emotion recognition using physiological signals is presented. Firstly the problems with acquisition of physiological signals related to specific human emotions are described. It is not a trivial problem to elicit real ...
Recognizing emotions expressed by body pose
Research into the visual perception of human emotion has traditionally focused on the facial expression of emotions. Recently researchers have turned to the more challenging field of emotional body language, i.e. emotion expression through body pose and ...
Ranking-Based Affect Estimation of Motion Capture Data in the Valence-Arousal Space
MOCO '18: Proceedings of the 5th International Conference on Movement and ComputingAffect estimation consists of building a predictive model of the perceived affect given stimuli. In this study, we are looking at the perceived affect in full-body motion capture data of various movements. There are two parts to this study. In the first ...
Comments