A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark

TL;DR

This paper critically examines current age estimation evaluation practices, revealing that many reported improvements are negligible compared to other factors, and proposes a unified benchmark using FaRL as the backbone model.

Contribution

It identifies issues in existing evaluation protocols, provides a comprehensive comparative analysis, and introduces a unified benchmark with FaRL for more reliable age estimation assessment.

Findings

Performance differences are negligible compared to other factors.

Evaluation protocols have persistent issues affecting reliability.

FaRL-based model outperforms previous methods on public datasets.

Abstract

Comparing different age estimation methods poses a challenge due to the unreliability of published results stemming from inconsistencies in the benchmarking process. Previous studies have reported continuous performance improvements over the past decade using specialized methods; however, our findings challenge these claims. This paper identifies two trivial, yet persistent issues with the currently used evaluation protocol and describes how to resolve them. We offer an extensive comparative analysis for state-of-the-art facial age estimation methods. Surprisingly, we find that the performance differences between the methods are negligible compared to the effect of other factors, such as facial alignment, facial coverage, image resolution, model architecture, or the amount of data used for pretraining. We use the gained insights to propose using FaRL as the backbone model and demonstrate its effectiveness on all public datasets. We make the source code and exact data splits public on GitHub.