# Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition
## 4 Experiments
### Evaluations on Multiple Benchmark Datasets
First, 3000 positive face pairs with age gaps were selected from LFW, in which the age gaps of most positive pairs are larger than ten years. The average age gap is about 20 years. Then 3000 negative pairs with the same gender and race were selected to reduce the influence of different attributes [36]. We followed the same protocol as the LFW, in which the dataset is divided into ten separate folds using the same identities contained in the ten folds of the LFW. Each fold contains 300 positive pairs and 300 negative pairs. We evaluated our method on CALFW, and the results are shown in Table 6. The results show that our proposed method is robust and reliable, even with larger age spans. **AgeDB. ** AgeDB [41] is a face dataset in the wild with large variations in pose, age, illumination, and expression. It contains 16,488 face images of 568 distinct subjects. Every image is annotated manually to achieve noise-free annotation of the age labels. It provides four protocols for age-invariant face verification protocols, wherein the age difference between each pair of faces is fixed to a predefined value, i.e., 5, 10, 20, or 30 years. The evaluation experiments on AgeDB-30 might best demonstrate our model's superiority for large age-span face verification since the 30-year age gap is the most challenging. Similar to the LFW [46], we strictly followed the protocol on AgeDB-30 to perform the 10-fold cross-validation, compute the face verification rate, and compare our results with other state-of-the-art CAFR methods. Table 7 shows the evaluation results from various methods on AgeDB-30. Most methods achieved performance higher than 90%, but the proposed model outperformed the other state-of-the-art CAFR methods. **FG-NET. ** FG-NET contains 1,002 face images from 82 subjects with ages ranging from 0 to 69. We experimented with the leave-one-out evaluation scheme adopted by HFA [43] \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline Center Loss [48] (2016) & 85.48 \\ SphereFace [37] (2017) & 90.30 \\ VGGFace2 [6] (2018) & 90.57 \\ Arcface [10] (2019) & 94.45 \\ AA\_CNN [36] (2021) & 90.7 \\ PMADN [38] (2021) & 91.20 \\ MTLFace [19] (2021) & 95.62 \\ IEF [31] (2022) & 95.82 \\ Ours & **96.10** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation results of the different methods on CALFW. The bold represents the best value. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline RJIVE [49] (2017) & 55.20 \\ VGG Face2 [6] (2018) & 89.89 \\ Center Loss [48] (2016) & 93.72 \\ SphereFace [37] (2017) & 91.70 \\ CosFace [50] (2018) & 94.56 \\ Arcface [10] (2019) & 95.15 \\ DAAE [51] (2020) & 95.30 \\ MTLFace [19] (2021) & 96.23 \\ Ours & **98.38** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluation results of the different methods on AgeDB-30. and Li et al. [52] to separate the training and testing data. We selected one image as the testing datum and fine-tuned the model on the other 1001 face images, repeating that process 1002 times. Considering that every subject in the dataset has multiple face images of different ages, that evaluation tactic can well reflect the performance of a face-recognition model. Table 8 and Figure 10b show the rank-1 recognition rate comparisons on the FG-NET dataset. Our method achieved good results (94.91%) and outperformed all the other state-of-the-art methods except for IEFP. Unlike our end-to-end model, the IEFP framework also trains an age estimation model, which increases the training time. We visualized some of the false identification results on FG-NET dataset in Figure 11. Note that the false identifications are mainly infants and children from 0 to 13 years old. As shown in Figure 6, 51.2% of the images in the benchmark FG-NET are from 0 to 13 years. Meanwhile, the CACD dataset used to train our model does not include images from that age period, which is disadvantageous for a data-driven-based method trying to learn the latent distributions of that particular age group. **LFW.