# Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition
## 4 Experiments
### Evaluations on Multiple Benchmark Datasets  
We evaluated our method on several benchmark cross-age datasets, CACD-VS, CALFW, AgeDB, and FG-NET, and a general face dataset, LFW [46]. Note that MORPH [47] was excluded because it was prepared for commercial use only. To evaluate the performance of our proposed method and compare it with other state-of-the-art CAFR methods, we \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Training Parameters of Networks (Millions)** & **Training No. of Images** \\ \hline DAL [30] & 54.73 & 313,986 \\ MTLFace [19] & 72.82 & 1,700,000 \\ Ours & 13.42 & 163,446 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of the training parameters and images required by different methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**K (Value)** & 25 & 50 & 75 & 100 \\ \hline
**Accuracy** & 99.60 & 99.60 & **99.63** & 98.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation results (%) on the CACD-VS dataset. The bold represents the best value. Figure 9: Face verification accuracy on the CACD-VS dataset with various values for \(\lambda_{1}\) and \(\lambda_{2}\). chose the verification accuracy and rank-1 identification rate as evaluation metrics. We used the receiver operating characteristic curve (ROC curve), which expresses the quality of the 1:1 matcher, to evaluate the verification accuracy. As shown in Figure 10a, we created the ROC curve on the cross-age datasets (CACD-VS, CALFW, and AgeDB) by plotting the true positive rate against the false positive rate. To evaluate the identification accuracy with the FG-NET dataset, we used the cumulative match curve (CMC) to measure the 1:k identification system performance. The evaluation schemes for the different datasets are described next. **CACD-VS.** The CACD-VS consists of 4000 face image pairs for face verification, 2000 positive and 2000 negative pairs. The age difference between most image pairs from the same person is less than nine years. We followed the pipeline suggested by [PERSON] et al. to calculate the similarity score of all the sample pairs [43]. We strictly followed the cross-validation rule [29] to calculate the similarity score for all sample pairs. We first divided the dataset into ten folds, with each fold containing 400 image pairs (200 positive pairs and 200 negative pairs) from 200 celebrities. We used nine of those ten folds to compute the threshold references and then used the best threshold to evaluate the last one fold. We repeated those experiments ten times for each of the ten folds and finally calculated the average accuracy. The evaluation results with the different methods on CACD-VS are summarized in Table 5.  
As shown in Table 5, the proposed method outperformed all the tested methods in a large-scale dataset, achieving an accuracy of 99.63%, which indicates the effectiveness  
Figure 10: Face recognition performance of our model (**a**) ROC curves on CACD-VS, CALFW, AgeDB-30. (**b**) CMC curve on FG-NET. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline LFCNN [27] (2016) & 98.5 \\ OE-CNN [29] (2018) & 99.20 \\ AIM [14] (2020) & 99.38 \\ DAL [30] (2019) & 99.40 \\ AA-CNN [36] (2021) & 99.20 \\ PMADN [38] (2021) & 99.20 \\ MTLFace [19] (2021) & 99.55 \\ IEFP [31] (2022) & 99.57 \\ Ours & **99.63** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation results of the different methods on CACD-VS. The bold represents the best value. of the proposed method. Note that the MTLFace method requires a large-scale dataset of almost 1.7 million faces for training, whereas ours used only 0.16 million faces. **CALFW. ** To demonstrate the effectiveness of our method for face recognition with a larger age span, we implemented a face verification experiment on the CALFW dataset. The CALFW dataset is an extension of the LFW dataset designed for unconstrained face verification with larger age gaps.