Facial Expression Form and Function

This research integrates computational models, subjective rating studies, and perceptual psychophysics to understand facial expressive behavior and its relation to emotion. We analyze static and dynamic configurations of muscle and surface features on the face for their egocentric function and perceptual significance.

Representative Publications

Chapman, H.A., Kim, D.A., Susskind, J.M. & Anderson, A.K. (2009). In Bad Taste: Evidence for the Oral Origins for Moral Disgust. Science 323(5918), p.1222-1226. [publisher]

Susskind, J.M., Lee, D.H., Cusi, A., Feiman, R., Grabski, W., and Anderson, A.K. (2008). Expressing fear enhances sensory acquisition. Nature Neuroscience 11, 843-850 (doi:10.1038/nn.2138). [pdf]

Analysis and synthesis in neural networks

The focus is to model high-level representations of faces (e.g. identity, expression, muscle actions) by analyzing databases containing thousands of images. Unsupervised learning is used to find statistical regularities for recognizing and synthesizing faces varying richly in expression and identity.

Representative Publications

Susskind, J.M., Hinton, G.E., Movellan, J.R., and Anderson, A.K. (2008). Generating Facial Expressions with Deep Belief Nets. To appear in J. Or (ed.) Affective Computing, Focus on Emotion Expression, Synthesis and Recognition. ARS Publishers. [pdf]

Nair, V., Susskind, J.M., and Hinton, G.E. (2008). Analysis-by-synthesis by Learning to Invert Generative Black Boxes. Artificial Neural Networks - ICANN 2008. [pdf]