Affective Computing – Publikationen

Maximilian P. Oppelt, Andreas Foltyn, Jessica Deuschel, Nadine R. Lang, Nina Holzer, Bjoern M. Eskofier, Seung Hee Yang: ADABase: A Multimodal Dataset for Cognitive Load Estimation, Sensors, 2022.

• Emanuel Slany, Yannik Ott, Stephan Scheele, Jan Paulus, Ute Schmid: CAIPI in Practice: Towards Explainable Interactive Medical Image Classification, IFIP International Conference on Artificial Intelligence Applications and Innovations, 2022
  
• Jaspar Pahl, Ines Rieger, Anna Möller, Thomas Wittenberg, and Ute Schmid. Female, white, 27? Bias Evaluation on Data and Algorithms for Affect Recognition in Faces. ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2022.
• Ines Rieger, Jaspar Pahl, Bettina Finzel, and Ute Schmid. CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition. 26th International Conference on Pattern Recognition (ICPR), 2022.

• Saha, B., Becker, L., Garbas, J. U., Oppelt, M., Foltyn, A., Hettenkofer, S., ... & Mahesh, B. (2021, March): Investigation of Relation between Physiological Responses and Personality during Stress Recovery. In 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (pp. 57-62). IEEE.
• Gjoreski, M., Mahesh, B., Kolenik, T., Uwe-Garbas, J., Seuss, D., Gjoreski, H., ... & Pejović, V. (2021): Cognitive load monitoring with wearables–lessons learned from a machine learning challenge. IEEE Access, 9, 103325-103336.
• Lang, N., Goes, N., Struck, M., Wittenberg, T., Seßner, J., Franke, J., ... & Naumann, S.: Evaluation of an algorithm for optical pulse detection in children for application to the Pepper robot. Article, 2021, Current Directions in Biomedical Engineering, 7(2), 484-487
• Jessica Deuschel, Bettina Finzel, and Ines Rieger. Uncovering the Bias in Facial Expressions. Kolloquium Forschende Frauen; Gender in Gesellschaft 4.0: Beiträge Bamberger Nachwuchswissenschaftlerinnen, 2021.
• Bettina Finzel, Ren´e Kollmann, Ines Rieger, Jaspar Pahl, and Ute Schmid. Deriving Temporal Protoypes from Saliency Map Clusters for the Analysis of Deep-Learning-based Facial Action Unit Classification. In Proc. of the FGKD Workshop at Lernen. Wissen. Daten. Analysen. (LWDA) Conference, 2021.
• J. Deuschel, D. Firmbach, C.I. Geppert, M. Eckstein, A. Hartmann, V. Bruns, P. Kuritcyn, J. Dexl, D. Hartmann, D.Perrin, Th. Wittenberg, M. Benz: Multi-Prototype Few-Shot Learning in Histopathology. 2021; proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops; pp. 620-628
• Andreas Foltyn and Jessica Deuschel. Towards reliable multimodal stress detection under distribution shift. In Companion Publication of the 2021 International Conference on Multimodal Interaction, pages 329–333, 2021
• Jessica Deuschel, Andreas Foltyn, Leonie Anna Adams, Jan Maximilian Vieregge, and Ute Schmid. Benchmarking robustness to natural distribution shifts for facial analysis. In NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications, 2021.

• Kirst, S., Naumann, S., Strehler, M., Lang, N., Seßner, J., & Dziobek, I.: Development of a Robot-Mediated Interaction Targeting Emotion Processing and Regulation in Children with Autism Spectrum Condition Using Emotion-Sensitive Technology. In INSAR 2020 Virtual Meeting. INSAR.
• Luguev, T., Seuß, D., & Garbas, J. U. (2020, March): Deep learning based affective sensing with remote photoplethysmography. In 2020 54th Annual Conference on Information Sciences and Systems (CISS) (pp. 1-4). IEEE. DOI: 9086200
• Jaspar Pahl, Ines Rieger, and Dominik Seuss: Multi-label Learning with Missing Values using Combined Facial Action Unit Datasets. The Art of Learning with Missing Values Workshop at International Conference on Machine Learning (ICML), 08/2020.
• Ines Rieger, Rene Kollmann, Bettina Finzel, Dominik Seuss, and Ute Schmid: Verifying Deep Learning-based Decisions for Facial Expression Recognition. In Proc. of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Conf.  (ESANN), 2020.
• Ines Rieger, Jaspar Pahl, and Dominik Seuss: Unique Class Group Based Multi-Label Balancing Optimizer for Action Unit Detection. In Proc. of the Affective Behavior Analysis in-the-wild (ABAW) Workshop of 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG). IEEE, 2020.

• Simon, M., Strehler, M., Kirst, S., Schmitt-Rüth, S., & Lang, N. (2019): Akzeptanzrisiken in der Mensch-Roboter-Interaktion-Nutzerzentrierte Erhebung von ethischen, rechtlichen und sozialen Implikationen (ELSI) am Beispiel einer Roboterplattform für Kinder mit eingeschränkten sozio-emotionalen Fähigkeiten. Mensch und Computer 2019; Workshopband.
• Ines Rieger, Bettina Finzel, Dominik Seuß, Thomas Wittenberg, and Ute Schmid. Make Pain Estimation Transparent: A Roadmap to Fuse Bayesian Deep Learning and Inductive Logic Programming (Poster). In Proc. of the 41st IEEE Engineering in Medicine & Biology Society Conference (EMBS), 2019.
• Ines Rieger, Thomas Hauenstein, Sebastian Hettenkofer, and Jens-Uwe Garbas. Towards Real-Time Head Pose Estimation: Exploring Parameter-Reduced Residual Networks on In-the-wild Datasets. In Proc. of the 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE), pages 123–134. Springer, 2019. oder https://arxiv.org/abs/1906.05203