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# Master's Thesis: Synthetic Faces for Age Estimation - Artifacts, Domain Shift, Label Quality

- **Company:** Fraunhofer-Gesellschaft
- **Location:** Darmstadt, Germany
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Neural Networks, Computer Vision, Python (Programming Language), Machine Learning, Pytorch, Generative AI, Wikis
- **Published:** October 29, 2026
- **Apply:** https://jobs.fraunhofer.de/talentcommunity/apply/1452462133/?locale=en_US

## About the Role

* Very good academic performance with an average grade of 2.2 or better (German grading scale)
 * Good knowledge of machine learning, computer vision and training neural networks
 * Ideally, knowledge of generative models or face analysis
 * Good Python skills, ideally with experience in PyTorch
 * Knowledge of GANs, diffusion models or image forensics is an advantage, but not a requirement
 * Motivation to independently familiarize yourself with new and current research topics
 * Interest in experimental research and the systematic analysis of neural networks
 * Interest in scientific research

## Description

* Generate and compare face images using different generative methods, for example StyleGAN-based models and modern diffusion or text-to-image models.
 * Systematically examine and quantify differences between real and synthetic face images. Possible approaches include classifiers for distinguishing real and synthetic images, feature space analyses and frequency domain analyses.
 * Identify and investigate generator-specific and cross-generator artifacts. In particular, it will be analyzed which features neural networks use to distinguish real from synthetic images.
 * Investigate the relationship between these artifacts and the performance of age estimation models. For this purpose, suitable ablation studies and controlled experiments are to be designed.
 * Develop or evaluate methods for reducing relevant artifacts in synthetic face images. Possible approaches include classical image processing, frequency domain filtering, learning-based methods, or domain adaptation and image-to-image translation.
 * Examine whether reducing the differences between real and synthetic images actually leads to better use of synthetic data when training age estimation models.
 * Investigate the quality of synthetic age labels. In particular, it will be analyzed how well the perceived age of generated persons matches the desired or specified age, and whether certain generators or age ranges show systematic deviations.
 * Examine the influence of incorrect age labels and visual artifacts as separately as possible, in order to determine which factor limits the use of synthetic data more strongly.
 * Derive, develop and evaluate new strategies for generating, selecting, post-processing or weighting synthetic training data based on the findings.

Results
The expected outcome is a systematic characterization of the differences between real and synthetic face images from various generative models. The thesis should show which artifacts or statistical properties make it possible to distinguish synthetic from real images, and to what extent these features are relevant for age estimation models.

Furthermore, methods for reducing the domain shift are to be developed or investigated, and their effects on training with mixed real and synthetic datasets evaluated. In particular, the thesis should clarify whether lower distinguishability between real and synthetic images also goes along with improved generalization of age estimation models.

Another focus is on the reliability of synthetic age labels. The thesis is expected to provide sound findings on how accurately different generative methods reproduce the desired ages, in which age ranges systematic errors occur, how these can be detected, and what influence this label noise has on training.

Based on this, the thesis should derive guidelines on when and under which conditions synthetic face images can add value for age estimation, and which requirements must be met. The developed methods will be evaluated on publicly available benchmark datasets such as UTKFace, APPA-REAL, AgeDB or IMDB-WIKI, as well as on suitable synthetically generated datasets such as HDA-SynChildFaces.

Be part of change

 * Research and structure literature on current topics in machine learning, computer vision and generative models
 * Generate and analyze synthetic face images using modern generative methods
 * Research and implement machine learning, computer vision and image forensics approaches
 * Develop suitable methods for analyzing and reducing artifacts in synthetic images
 * Plan and conduct controlled experiments and ablation studies
 * Train and evaluate neural networks for age estimation
 * Critically evaluate and interpret your results
 * Present your results
 * Write up your results in a scientific thesis in the form of a master's thesis

What you contribute