|
ABSTRACT
Insights from human perception of moving faces have the potential to provide interesting insights for technical animation systems as well as in the neural encoding of facial expressions in the brain. We present a psychophysical experiment that explores high-level after-effects for dynamic facial expressions. We address specifically in how far such after-effects represent adaptation in neural representation for static vs. dynamic features of faces. High-level after-effects have been reported for the recognition of static faces [Webster and Maclin 1999; Leopold et al. 2001], and also for the perception of point-light walkers [Jordan et al. 2006; Troje et al. 2006]. After-effects were reflected by shifts in category boundaries between different facial expressions and between male and female walks. We report on a new after-effect in humans observing dynamic facial expressions that have been generated by a highly controllable dynamic morphable face model. As key element of our experiment, we created dynamic 'anti-expressions' in analogy to static 'anti-faces' [Leopold et al. 2001]. We tested the influence of dynamics and identity on expression-specific recognition performance after adaptation to 'anti-expressions'. In addition, by a quantitative analysis of the optic flow patterns corresponding to the adaptation and test expressions we rule out that the observed changes reflect a simple low-level motion after-effect. Since we found no evidence for a critical role of temporal order of the stimulus frames we conclude that after-effects in dynamic faces might be dominated by adaptation to the form information in individual stimulus frames.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
| |
1
|
|
| |
2
|
Brainard, D. 1997. The psychophysics toolbox. Spatial Vision 4, 433--436.
|
| |
3
|
Burton, A., Bruce, V., and Hancock, P. 1999. From pixels to people: a model of familiar face recognition. Cognitive Science 23, 1--31.
|
| |
4
|
Chang, Y., Hu, H., and Turk, M. 2004. Probabilistic expression analysis on manifolds. In Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, vol. 2, 520--527.
|
| |
5
|
Choe, B., and Ko, H.-S. 2001. Analysis and synthesis of facial expressions with hand-generated muscle actuation basis. In Proceedings of Computer Animation, IEEE Computer Society, IEEE, 12--19.
|
 |
6
|
Cristóbal Curio , Martin Breidt , Mario Kleiner , Quoc C. Vuong , Martin A. Giese , Heinrich H. Bülthoff, Semantic 3D motion retargeting for facial animation, Proceedings of the 3rd symposium on Applied perception in graphics and visualization, July 28-29, 2006, Boston, Massachusetts
[doi> 10.1145/1140491.1140508]
|
| |
7
|
Ekman, P., and Friesen, W. 1978. Facial action coding system: A technique for the measurement of facial movement. Consulting Psychologists Press.
|
| |
8
|
Fox, C., and Barton, J. 2006. What is adapted in face adaptation? the neural representations of expression in the human visual system. Brain Research 1127, 80--89.
|
| |
9
|
Giese, M., and Leopold, D. A. 2005. Physiologically inspired neural model for the encoding of face spaces. Neurocomputing 65--66 (June), 93--101.
|
| |
10
|
Giese, M., and Poggio, T. 2003. Neural mechanisms for the recognition of biological movements and action. Nature Review Neuroscience 4, 179--192.
|
| |
11
|
|
| |
12
|
Hill, H., Troje, N., and Johnston, A. 2005. Range- and domain-specific exaggeration of facial speech. Journal of Vision 5, 10 (12), 793--807.
|
| |
13
|
Jiang, X., Rosen, E., Zeffiro, T., Vanmeter, J., Blanz, V., and Riesenhuber, M. 2006. Evaluation of a shape-based model of human face discrimination using fmri and behavioral techniques. Neuron 50, 1, 159--72.
|
| |
14
|
Jordan, H., Fallah, M., and Stoner, G. 2006. Adaptation of gender derived from biological motion. Nature Neuroscience 9, 738--739.
|
| |
15
|
Leopold, D., O'Toole, A., Vetter, T., and Blanz, V. 2001. Prototype-referenced shape encoding revealed by high-level after effects. Nature Neuroscience 4, 89--94.
|
| |
16
|
Leopold, D., Bondar, I., and Giese, M. 2006. Norm-based face encoding by single neurons in the monkey inferotemporal cortex. Nature 442, 7102 (July), 572--575.
|
| |
17
|
Loeffler, G., Yourganov, G., Wilkinson, F., and Wilson, H. R. R. 2005. fmri evidence for the neural representation of faces. Nature Neuroscience (September).
|
| |
18
|
O'Toole, A., Roark, D., and Abdi, H. 2002. Recognizing moving faces: a psychological and neural synthesis. Trends in Cognitive Science 6, 6, 261--266.
|
| |
19
|
Troje, N., Sadr, J., Geyer, H., and Nakayama, K. 2006. Adaptation aftereffects in the perception of gender from biological motion. Journal of Vision 6, 8, 850--857.
|
| |
20
|
Valentine, T. 1991. A unified account of the effects of distinctiveness, inversion and race in face recognition. Quarterly J. Experimental Psychology 43, 161204.
|
 |
21
|
|
| |
22
|
Webster, M., and Maclin, O. 1999. Figural after-effects in the perception of faces. Psychonomic Bulletin and Review 6, 647--653.
|
|