Modelling and Investigation of Facial Expression Perception

Modelling and Investigation of Facial Expression Perception

Research Area:

Neural and Computational Principles of Action and Social Processing


Martin A. Giese; Nick Taubert; Michael Stettler;


Aleix Martinez (Ohio State University); Peter Thier; Peter Dicke; Silvia Spadacenta; Ramona Siebert; Marius Görner


Dynamic faces are essential for the communication of humans and non-human primates. However, the exact neural circuits of their processing remain unclear. Based on previous models for cortical neural processes involved for social recognition (of static faces and dynamic bodies), we propose  a norm-based mechanism, relying on neurons that represent dierences between the actual facial shape and the neutral facial pose. While popular neural network models predict a joint encoding of facial shape and dynamics, the neuromuscular control of faces evolved more slowly than facial shape, suggesting a separate encoding. To investigate these alternative hypotheses, we developed photo-realistic human and monkey heads that were animated with motion capture data from monkeys and humans. Exact control of expression dynamics was accomplished by a Bayesian machine-learning technique.



Shape-invariant encoding of dynamic primate facial expressions in human perception

In this project we want to develop highly controllable face stimuli to study the neural basis of face processing and the analyses of the dynamics and structure of facial movements.

Multi-Domain Norm-Referenced Encoding

Biologically-inspired mechanism for such transfer learning, which is based on norm-referenced encoding, where patterns are encoded in terms of difference vectors relative to a domain-specific reference vector.


Taubert, N., Stettler, M., Siebert, R., Spadacenta, S., Sting, L., Dicke, P. et al. (2021). Shape-invariant encoding of dynamic primate facial expressions in human perception. eLife. [More] 
Stettler, M., Taubert, N., Siebert, R., Spadacenta, S., Dicke, P., Thier, P. et al (2021). Neural models for the (cross-species) recognition of dynamic facial expressions. Göttingen Meeting of the German Neuroscience Society 2021, Germany . [More] 
Siebert, R., Taubert, N., Spadacenta, S., Dicke, P. W., Giese, M. A. & Thier, P (2020). A Naturalistic Dynamic Monkey Head Avatar Elicits Species-Typical Reactions and Overcomes the Uncanny Valley . [More] 
Stettler, M., Taubert, N., Azizpour, T., Siebert, R., Spadacenta, S., Dicke, P. et al. (2020). Physiologically-inspired Neural Circuits for the Recognition of Dynamic Faces. Artificial Neural Networks and Machine Learning – ICANN 2020 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I. Springer, Berlin(168-179). [More]