Towards Real-Time Head Pose Estimation: Exploring Parameter-Reduced Residual Networks on In-the-wild Datasets. - Ines Rieger; Thomas Hauenstein; Sebastian Hettenkofer; Jens-Uwe Garbas (IEA/AIE, 2019)

Verifying Deep Learning-based Decisions for Facial Expression Recognition. - Ines Rieger; Rene Kollmann; Bettina Finzel; Dominik Seuss; Ute Schmid (ESANN, 2020)

Unique Class Group Based Multi-Label Balancing Optimizer for Action Unit Detection. - Ines Rieger; Jaspar Pahl; Dominik Seuss (FG, 2020)

Multi-Label Class Balancing Algorithm for Action Unit Detection. - Jaspar Pahl; Ines Rieger; Dominik Seuss (CoRR, 2020)

Multi-label Learning with Missing Values using Combined Facial Action Unit Datasets. - Jaspar Pahl; Ines Rieger; Dominik Seuss (CoRR, 2020)

Uncovering the Bias in Facial Expressions. - Jessica Deuschel; Bettina Finzel; Ines Rieger (CoRR, 2020)

Deriving Temporal Prototypes from Saliency Map Clusters for the Analysis of Deep-Learning-based Facial Action Unit Classification. - Bettina Finzel; René Kollmann; Ines Rieger; Jaspar Pahl; Ute Schmid (LWDA, 2021)

Female, white, 27? Bias Evaluation on Data and Algorithms for Affect Recognition in Faces. - Jaspar Pahl; Ines Rieger; Anna Möller; Thomas Wittenberg; Ute Schmid (FAccT, 2022)

CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition. - Ines Rieger; Jaspar Pahl; Bettina Finzel; Ute Schmid (ICPR, 2022)

Domain-Specific Evaluation of Visual Explanations for Application-Grounded Facial Expression Recognition. - Bettina Finzel; Ines Rieger; Simon Kuhn; Ute Schmid (CD-MAKE, 2023)

Investigating the Regularization of Deep Neural Networks for Affect Recognition with Relevance-Guided Local Explanations. - Ines Rieger (AIMSA, 2024)

An Embedding is Worth a Thousand Noisy Labels. - Francesco Di Salvo; Sebastian Doerrich; Ines Rieger; Christian Ledig (Trans. Mach. Learn. Res., 2025)