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M. Sc. Stettler, Michael

Section for Computational Sensomotorics
Department of Cognitive Neurology
Hertie Institute for Clinical Brain Research
Centre for Integrative Neuroscience
University Clinic Tübingen
Otfried-Müller-Str. 25
72076 Tübingen, Germany
Michael Stettler

Projects

Publications

Stettler, M. (2025). Investigation and Modelling of Dynamical Facial Expression Perception. Phd Thesis.
Investigation and Modelling of Dynamical Facial Expression Perception
Research Areas: Uncategorized
Type of Publication: Phd Thesis
Stettler, M., Lappe, A., Taubert, N. & Giese, M. A. (2023). Multi-Domain Norm-referenced Encoding Enables Data Efficient Transfer Learning of Facial Expression Recognition..
Multi-Domain Norm-referenced Encoding Enables Data Efficient Transfer Learning of Facial Expression Recognition
Abstract:

People can innately recognize human facial expressions in unnatural forms, such as when depicted on the unusual faces drawn in cartoons or when applied to an animal’s features. However, current machine learning algorithms struggle with out-of-domain transfer in facial expression recognition (FER). We propose a 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. By incorporating domain-specific reference frames, we demonstrate high data efficiency in transfer learning across multiple domains. Our proposed architecture provides an explanation for how the human brain might innately recognize facial expressions on varying head shapes (humans, monkeys, and cartoon avatars) without extensive training. Norm-referenced encoding also allows the intensity of the expression to be read out directly from neural unit activity, similar to face-selective neurons in the brain. Our model achieves a classification accuracy of 92.15% on the FERG dataset with extreme data efficiency. We train our proposed mechanism with only 12 images, including a single image of each class (facial expression) and one image per domain (avatar). In comparison, the authors of the FERG dataset achieved a classification accuracy of 89.02% with their FaceExpr model, which was trained on 43,000 images.

Type of Publication: Misc
Stettler, M., Lappe, A., Siebert, R., Taubert, N., Thier, P. & Giese, M. A (2023). Norm-referenced encoding of facial expressions facilitates transfer learning to novel head shapes. 2023 Neuroscience Meeting Planner . Washington, D.C..
Norm-referenced encoding of facial expressions facilitates transfer learning to novel head shapes
Authors: Stettler, Michael; Lappe, Alexander; Siebert, Ramona Taubert, Nick; Thier, Peter Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
JRESEARCH_BOOK_TITLE: 2023 Neuroscience Meeting Planner
Organization: Society for Neuroscience
Address: Washington, D.C.
Bognár, A., Mukovskiy, A., Nejad, G. G., Taubert, N., Stettler, M., Martini, L. M. et al (2023). Simultaneous recordings from posterior and anterior body responsive regions in the macaque Superior Temporal Sulcus . VSS 2023, May 19-24 2023, St. Pete Beach, Florida.
Simultaneous recordings from posterior and anterior body responsive regions in the macaque Superior Temporal Sulcus
Type of Publication: In Collection
Bognár, A., Mukovskiy, A., Nejad, G. G., Taubert, N., Stettler, M., Martini, L. M. et al (2023). Feature selectivity of body-patch neurons assessed with a large set of monkey avatars . 13th Annual Meeting on PrimateNeurobiology, Apr.26-28 2023, Göttingen Primate Center..
Feature selectivity of body-patch neurons assessed with a large set of monkey avatars
Type of Publication: In Collection
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