## 4 Experiments  
The FFHQ-Aging dataset is used to perform the experiments [40]. FFHQ-Aging is an extension of the original FFHQ, but adds tags for each image with gender, age group, head posture, whether they wear glasses, and other tags, allowing images to be segmented into subsets and used for experiments. To carry out the experiments, it was decided to use the FFHQ dataset in its \(256\,\times\,256\) pixel version, since it is a resolution large enough to be able to appreciate details and nuances in the faces of people. On the other hand, being a moderate resolution, it allows for faster training than if the network had to be trained to generate \(1024\,\times\,1024\) pixels. Table 4 presents a comparison based on the results obtained when performing retraining with the data segmented by age segment, and size of the dataset, where it is possible to  
observe that the results for the FID are very different from those that we can observe in the original paper, where the FID obtained is at 2.84. Figure 6 shows a collage of images generated by StyleGAN2 and StyleGAN3, respectively. This example highlights an important point in the evaluation of generative models: although the FID values obtained by each model may be very good and close to zero, these values alone do not guarantee the perceptual quality of the generated images. An FID close to zero indicates that the feature distributions of the original and synthetic datasets are very similar to each other. However, human perception of image quality is not based solely on statistical similarity to real data. It is important to note that the quality of a generated image can be affected by a number of factors, such as visual artifacts, lack of consistency in the image, or the presence of details that appear inconsistent. In the case of the images generated by StyleGAN2 and StyleGAN3, although the FID values may be excellent, visual perception reveals that there are still defects perceptible to the human eye. This underlines the importance of not relying solely on evaluation metrics, such as FID, to judge the quality of the generated images. Human assessment remains critical in determining the actual quality of images and their fidelity. However, if we take care and segment the data by age, and truncate part of the statistical distribution, taking out the outliers, we can obtain higher FID values, but with \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Segment** & **Size** & **Average FID \(\downarrow\)2** & **Min FID** & **\#\% FID** \\ \hline
0–2 & 2492 & 27.64 & 14.81 & 0 \\
70+ & 1812 & 22.30 & 15.47 & 19.32 \\
10–14 & 2235 & 19.94 & 16.09 & 10.61 \\
3–6 & 4523 & 18.85 & 13.00 & 5.46 \\
7–9 & 2858 & 18.30 & 14.51 & 2.90 \\
15–19 & 4022 & 13.51 & 11.89 & 26.18 \\
50–69 & 7726 & 9.62 & 6.83 & 28.79 \\
40–49 & 9678 & 8.58 & 7.02 & 10.80 \\
20–29 & 19,511 & 8.13 & 6.56 & 5.25 \\
30–39 & 15,143 & 7.73 & 6.35 & 4.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of training sessions carried out with different age segments. Figure 6: A selection of synthetic images produced using StyleGAN2 with an FID of 2.84 and StyleGAN3 with an FID of 2.79, exhibiting inherent imperfections or flaws. more accurate results, that is, images that do not present defects or imperfections and that, to the human eye, qualify as real. A positive trend is observed in all age segments with a greater provision of training images. Throughout the experiments, an observed threshold of approximately 10,000 images is the minimum requirement to achieve FID values less than 10. In particular, when the image count exceeds 10,000, a direct correlation with FID becomes apparent. Previous experiments show that the generated images are devoid of blemishes or defects. This underscores the importance of understanding the available data. In addition, models of this nature require substantial image volumes.