Master's Thesis: Modelling Approaches and Loss Design for Precise Age Estimation

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 ap...