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. We argue that, for age estimation tasks outside of the low-data regime, designing specialized methods is unnecessary, and the standard approach of utilizing cross-entropy loss is sufficient. This paper aims to address the benchmark shortcomings by evaluating state-of-the-art age estimation methods in a unified and comparable setting. We systematically analyze the impact of various factors, including facial alignment, facial coverage, image resolution, image representation, model architecture, and the amount of data on age estimation results. Surprisingly, these factors often exert a more significant influence than the choice of the age estimation method itself. We assess the generalization capability of each method by evaluating the cross-dataset performance for publicly available age estimation datasets. The results emphasize the importance of using consistent data preprocessing practices and establishing standardized benchmarks to ensure reliable and meaningful comparisons. The source code is available at https://github.com/paplhjak/Facial-Age-Estimation-Benchmark.

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