Lisa Gralewski
ABSTRACT
This paper presents a novel technique for the generation of 'video textures' to display human emotion. This is achieved by a method which uses existing video footage to synthesise new sequences of coherent facial expression and head motions.An 'expression space' which is defined by sets of emotion models is constructed using principal components analysis (PCA) and the application of an auto-regressive process (ARP). Using this expression space a tool has been developed which enables this space to be navigated, and to view new video sequences generated from it. This tool provides an intuitive interface, which allows a nonspecialist user easy traversal of the highly dimensional expression space and enables them to select an emotion and automatically generate new facial expression sequences.
- B. ABBOUD, F. DAVOINE, M. D. 2003. Statistical modelling for facial expression analysis and synthesis.Google Scholar
- BOOKSTEIN, F. L. 1989. Principle warps: Thin plate splines and the decomposition of deformations. In IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. II(6), 567--585. Google ScholarDigital Library
- CAMPBELL, N. W., DALTON, C. J., GIBSON, D. P., AND THOMAS, B. T. 2002. Practical generation of video textures using the auto-regressive. In Proceedings of the British Machine Vision Conference 2002, BMVA, P. L. Rosin and D. Marshall, Eds., 434--443. Google ScholarDigital Library
- COHEN, I., GARG, A., AND HUANG, T., 2000. Emotion recognition from facial expressions using multilevel hmm.Google Scholar
- COOTES, T., AND TAYLOR, C. 1999. Statistical models of appearance for computer vision. Tech. rep.Google Scholar
- COOTES, T. F., EDWARDS, G. J., AND TAYLOR, C. J. 1998. Active appearance models. In European Conference on Computer Vision, springer, H. Burkhardt and B. Neumann, Eds., vol. 2, 484--498. Google ScholarDigital Library
- DI FIORE, F., AND VAN REETH, F. 2003. Mimicing 3D transformations of emotional stylized animation with minimal 2D input. In Proceedings of GRAPHITE 2003, 21--28. Google ScholarDigital Library
- DONATO, G., BARTLETT, M. S., HAGER, J. C., EKMAN, P., AND SEJNOWSKI, T. J. 1999. Classifying facial actions. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 10, 974--989. Google ScholarDigital Library
- ESSA, I., AND BASU, S., 1996. Modeling, tracking and interactive animation of facial expressions and head movements using input from video. Google ScholarDigital Library
- EVANS, D. 2001. Emotion. Oxford University Press.Google Scholar
- FLEXER, A. 1999. On the use of self-organizing maps for clustering and visualization. In Principles of Data Mining and Knowledge Discovery, 80--88. Google ScholarDigital Library
- GIBSON, D. P., CAMPBELL, N. W., DALTON, C. J., AND THOMAS, B. T. 2000. Extraction of motion data from image sequences to assist animators. In Proceedings of the British Machine Vision Conference 2000, BMVA, M. Mirmehdi and B. Thomas, Eds., 302--311. Google ScholarDigital Library
- KANADE, COHN, T. 2000. Comprehensive database for facial expression analysis. Google ScholarDigital Library
- KATSIKITIS, M. 2003. The Human Face. Kluwer Academic Publishers.Google Scholar
- OATES, T., FIROIU, L., AND COHEN, P., 1999. Clustering time series with hidden markov models and dynamic time warping.Google Scholar
- PADGETT, C., AND COTTRELL, G., 1995. Identifying emotion in static face images.Google Scholar
- RUTTKAY, Z., NOOT, H., AND TEN HAGEN, P., 2003. Emotion disc and emotion squares: Tools to explore the facial expression space.Google Scholar
- SCHNEIDER, T., AND NEUMAIER, A. 2001. Algorithm 808: Arfit - a mat-lab package for the estimation of parameters and eigenmodes of multivariate autoregressive models. In ACM Transactions on Mathematical Software, vol. 27, 58--65. Google ScholarDigital Library
- SCHÄODL, A., SZELISKI, R., SALESIN, D. H., AND ESSA, I. 2000. Video textures. In Siggraph 2000, Computer Graphics Proceedings, ACM Press / ACM SIGGRAPH / Addison Wesley Longman, K. Akeley, Ed., 489--498. Google ScholarDigital Library
- TURAGA, D., AND CHEN, T. 2002. Face recognition using mixtures of principal components. In International conference on image processing.Google ScholarCross Ref
- TURK, AND PENTLAND. 1991. Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 1, 71--86.Google ScholarDigital Library
- VICON, 2003. http://www.vicon.com/entertainment/index.shtml.Google Scholar
Index Terms
- Statistical synthesis of facial expressions for the portrayal of emotion
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