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Felix Kuhnke - IEEE Xplore Author Profile
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Felix Kuhnke

Publications
8
Citations
97
Publications by Year
20172024
Co-Authors:
Show All Co-Authors (9)
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Felix Kuhnke

Affiliation

Institute of Mechatronic Systems, Leibniz Universität Hannover

Publication Topics

Biography

Felix Kuhnke received the M.Sc. degree in media technology from the Technische Universität Ilmenau in 2014. He is currently pursuing the Ph.D. degree with the Institut für Informationsverarbeitung, Leibniz University Hannover, where he is also currently working as a Research Assistant. His research interests include analysis and synthesis of human faces, machine learning, domain adaptation, virtual avatars, and facial interaction.(Based on document published on 19 January 2023).
Publications
8
Citations
97
Publications by Year
20172024
Co-Authors:
Show All Co-Authors (9)
Author's Published Works

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Distribution-Aware Multi-Label FixMatch for Semi-Supervised Learning on CheXpert

Sontje Ihler;Felix Kuhnke;Timo Kuhlgatz;Thomas Seel

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
Semi-supervised learning (SSL) has achieved remarkable success for multiclass classification in recent years, yielding a promising solution for medical image classification where labeled data is scarce but unlabeled images are accessible. In the context of multi-label problems however, SSL is still under-explored. In this work we adapt Fix-Match to the multi-label scenario, specifically focusing o...Show More

Distribution-Aware Multi-Label FixMatch for Semi-Supervised Learning on CheXpert

Sontje Ihler;Felix Kuhnke;Timo Kuhlgatz;Thomas Seel

Year: 2024 | Conference Paper |

Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency

Felix Kuhnke;Jörn Ostermann

IEEE Transactions on Biometrics, Behavior, and Identity Science
Year: 2023 | Volume: 5, Issue: 3 | Journal Article |
Cited by: Papers (2)
Head pose estimation plays a vital role in biometric systems related to facial and human behavior analysis. Typically, neural networks are trained on head pose datasets. Unfortunately, manual or sensor-based annotation of head pose is impractical. A solution is synthetic training data generated from 3D face models, which can provide an infinite number of perfect labels. However, computer generated...Show More

Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency

Felix Kuhnke;Jörn Ostermann

Year: 2023 | Volume: 5, Issue: 3 | Journal Article |

Relative Pose Consistency for Semi-Supervised Head Pose Estimation

Felix Kuhnke;Sontje Ihler;Jörn Ostermann

2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Human head pose estimation from images plays a vital role in applications like driver assistance systems and human behavior analysis. Head pose estimation networks are typically trained in a supervised manner. Unfortunately, manual/sensor-based annotations of head poses are prone to errors. A solution is supervised training on synthetic training data generated from 3D face models which can provide...Show More

Relative Pose Consistency for Semi-Supervised Head Pose Estimation

Felix Kuhnke;Sontje Ihler;Jörn Ostermann

Year: 2021 | Conference Paper |

Two-Stream Aural-Visual Affect Analysis in the Wild

Felix Kuhnke;Lars Rumberg;Jörn Ostermann

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
Year: 2020 | Conference Paper |
Cited by: Papers (49)
Human affect recognition is an essential part of natural human-computer interaction. However, current methods are still in their infancy, especially for in-the-wild data. In this work, we introduce our submission to the Affective Behavior Analysis in-the-wild (ABAW) 2020 competition. We propose a two-stream aural-visual analysis model to recognize affective behavior from videos. Audio and image st...Show More

Two-Stream Aural-Visual Affect Analysis in the Wild

Felix Kuhnke;Lars Rumberg;Jörn Ostermann

Year: 2020 | Conference Paper |

Deep Head Pose Estimation Using Synthetic Images and Partial Adversarial Domain Adaption for Continuous Label Spaces

Felix Kuhnke;Joern Ostermann

2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019 | Conference Paper |
Cited by: Papers (36)
Head pose estimation aims at predicting an accurate pose from an image. Current approaches rely on supervised deep learning, which typically requires large amounts of labeled data. Manual or sensor-based annotations of head poses are prone to errors. A solution is to generate synthetic training data by rendering 3D face models. However, the differences (domain gap) between rendered (source-domain)...Show More

Deep Head Pose Estimation Using Synthetic Images and Partial Adversarial Domain Adaption for Continuous Label Spaces

Felix Kuhnke;Joern Ostermann

Year: 2019 | Conference Paper |

Unsupervised Features for Facial Expression Intensity Estimation Over Time

Maren Awiszus;Stella Graßhof;Felix Kuhnke;Jörn Ostermann

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (4)
The diversity of facial shapes and motions among persons is one of the greatest challenges for automatic analysis of facial expressions. In this paper, we propose a feature describing expression intensity over time, while being invariant to person and the type of performed expression. Our feature is a weighted combination of the dynamics of multiple points adapted to the overall expression traject...Show More

Unsupervised Features for Facial Expression Intensity Estimation Over Time

Maren Awiszus;Stella Graßhof;Felix Kuhnke;Jörn Ostermann

Year: 2018 | Conference Paper |

Visual speech synthesis from 3D mesh sequences driven by combined speech features

Felix Kuhnke;Jörn Ostermann

2017 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2017 | Conference Paper |
Cited by: Papers (3) | Patents (1)
Given a pre-registered 3D mesh sequence and accompanying phoneme-labeled audio, our system creates an animatable face model and a mapping procedure to produce realistic speech animations for arbitrary speech input. Mapping of speech features to model parameters is done using random forests for regression. We propose a new speech feature based on phonemic labels and acoustic features. The novel fea...Show More

Visual speech synthesis from 3D mesh sequences driven by combined speech features

Felix Kuhnke;Jörn Ostermann

Year: 2017 | Conference Paper |

Projective structure from facial motion

Stella Graßhof;Hanno Ackermann;Felix Kuhnke;Jörn Ostermann;Sami S. Brandt

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)
Year: 2017 | Conference Paper |
Cited by: Papers (2)
Nonrigid Structure-From-Motion is a well-known approach to estimate time-varying 3D structures from 2D input image sequences. For challenging problems such as the reconstruction of human faces, state-of-the-art approaches estimate statistical shape spaces from training data. It is common practice to use orthographic or weak-perspective camera models to map 3D to 2D points. We propose to use a proj...Show More

Projective structure from facial motion

Stella Graßhof;Hanno Ackermann;Felix Kuhnke;Jörn Ostermann;Sami S. Brandt

Year: 2017 | Conference Paper |

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