Abstract
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 two alternative neural models for the recognition of dynamic faces: (i) an example-based mechanism that encodes dynamic facial expressions as sequences of learned keyframes using a recurrent neural network (RNN), and (ii) a norm-based mechanism, relying on neurons that represent differences between the actual facial shape and the neutral facial pose. We tested both models exploiting highly controlled facial monkey expressions, generated using a photo-realistic monkey avatar that was controlled by motion capture data from monkeys. We found that both models account for the recognition of normal and temporally reversed facial expressions from videos. However, if tested with expression morphs, and with expressions of reduced strength, both models made quite different prediction, the norm-based model showing an almost linear variation of the neuron activities with the expression strength and the morphing level for cross-expression morphs, while the example based model did not generalize well to such stimuli. These predictions can be tested easily in electrophysiological experiments, exploiting the developed stimulus set.
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Acknowledgements
This work was supported by HFSP RGP0036/2016 and EC CogIMon H2020 ICT-23-2014/644727. It was also supported by BMBF FKZ 01GQ1704, BW-Stiftung NEU007/1 KONSENS-NHE, ERC 2019-SyG-RELEVANCE-856495 and the Deutsche Forschungsgesellschaft Grant TH425/12-2. NVIDIA Corp.
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Stettler, M. et al. (2020). Physiologically-Inspired Neural Circuits for the Recognition of Dynamic Faces. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12396. Springer, Cham. https://doi.org/10.1007/978-3-030-61609-0_14
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