Project description
Background/Motivation:Face-based age estimation is central to many applications (e.g., identity verification, youth protection, medicine). Classical approaches (pure regression or simple classification) have clear limitations, however: they ignore uncertainty, suffer from imbalanced data (long tail, missing age classes), and the non-linear scale of age. At the same time, a recently published study claims that the choice of loss function and architecture only has a limited impact on performance [7]. This blanket statement is to be critically and thoroughly examined in this work to clarify when the choice of loss and architecture is decision-relevant and when it remains secondary.Objective: The aim of this master's thesis is a systematic comparison of key modelling approaches and loss functions for age estimation, as well as the development of new, robust methods.To this end: Approaches to be implemented and compared: classical point regression, probabilistic regression, and quantile regression [4], classification [1], ordinal classification [2,3], label distribution learning [5], hybrid methods, etc. Conditions and problematic cases to be examined: missing or small age classes, strong imbalance (long distribution tails), label noise, non-linear age scale. Existing metrics are critically examined and suitable metrics are identified or developed. New procedures will be designed and evaluated based on the new insights. Results: Robust guidelines are expected on when which approach works (or fails), including ablation studies on loss design, binning/ordinalization, distribution targets, and hybridisations. The work provides reproducible implementations, strong baselines, extensive evaluation on common datasets (e.g., UTKFace, IMDB-WIKI, APPA-REAL, MORPH II, AgeDB), as well as proposals for new, more robust methods. The work presents robust guidelines on when which approach works or does not work, limitations and pitfalls, and unexpected results. The methods are evaluated and compared using publicly available benchmark datasets and self-developed scenarios. The code used is well-documented, reusable, and the results are reproducible. Be part of change Researching and structuring the literature on a current topic in the field of machine learning. Researching and implementing novel machine learning and computer vision approaches. Planning and conducting experiments. Self-critical evaluation of the obtained results. Presenting the results. Preparing a scientific paper in the form of a master's thesis with the results.
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