Apparent Age Estimation: Challenges and Outcomes

ArXi:2604.03335v1 Announce Type: new Apparent age estimation is a valuable tool for business personalization, yet current models frequently exhibit graphic biases. We review prior works on the DEX method by applying distribution learning techniques such as Mean-Variance Loss (MVL) and Adaptive Mean-Residue Loss (AMRL), and evaluate them in both accuracy and fairness. Using IMDB-WIKI, APPA-REAL, and FairFace, we nstrate that while AMRL achieves state-of-the-art accuracy, trade-offs between precision and graphic equity persist.