Apparent Age Estimation: Challenges and Outcomes

TL;DR

This paper reviews apparent age estimation models, highlighting biases and trade-offs between accuracy and fairness, and emphasizes the need for diverse datasets and fairness validation.

Contribution

It applies distribution learning techniques to evaluate bias and accuracy trade-offs in apparent age estimation models, revealing persistent demographic disparities.

Findings

AMRL achieves state-of-the-art accuracy

Biases persist for Asian and African American groups

Feature focus varies across demographics

Abstract

Apparent age estimation is a valuable tool for business personalization, yet current models frequently exhibit demographic 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 demonstrate that while AMRL achieves state-of-the-art accuracy, trade-offs between precision and demographic equity persist. Despite clear age clustering in UMAP embeddings, our saliency maps indicate inconsistent feature focus across demographics, leading to significant performance degradation for Asian and African American populations. We argue that technical improvements alone are insufficient; accurate and fair apparent age estimation requires the integration of localized and diverse datasets, and strict adherence to fairness validation protocols.