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Comparing different age estimation methods poses a challenge due to theunreliability of published results stemming from inconsistencies in thebenchmarking process. Previous studies have reported continuous performanceimprovements over the past decade using specialized methods; however, ourfindings challenge these claims. This paper identifies two trivial, yetpersistent issues with the currently used evaluation protocol and describes howto resolve them. We describe our evaluation protocol in detail and providespecific examples of how the protocol should be used. We utilize the protocolto offer an extensive comparative analysis for state-of-the-art facial ageestimation methods. Surprisingly, we find that the performance differencesbetween the methods are negligible compared to the effect of other factors,such as facial alignment, facial coverage, image resolution, modelarchitecture, or the amount of data used for pretraining. We use the gainedinsights to propose using FaRL as the backbone model and demonstrate itsefficiency. The results emphasize the importance of consistent datapreprocessing practices for reliable and meaningful comparisons. We make oursource code public athttps://github.com/paplhjak/Facial-Age-Estimation-Benchmark.

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