## 4 Experimental Results
### Performance Verification Experiment
#### 4.2.5 Cross-Dataset Comparison with Multiple Advanced Models  
To ensure that the model can correctly route the images, we trained a binary classification convolutional neural network for occlusion detection. The model's class labels were set to \"face\" and \"occluded_face\" to allow the model to effectively distinguish between faces with and without occlusions. In the network architecture, the input layer accepts color face images of size 224 \(\times\) 224 pixels. Features are then extracted through multiple convolutional and pooling layers, followed by a global-average pooling layer that converts the feature map into a one-dimensional vector. The output layer contains two neurons, using the Sigmoid activation function to output the probability for each class. During training, binary cross-entropy was used as the loss function to optimize the model's ability to distinguish between the two classes. The optimizer was Adam with an initial learning rate of 0.001, a training batch size of 32, and 50 epochs. To enhance the model's generalization ability, we applied various data augmentations, including random rotations, flips, and color jittering. The performance on the validation set was monitored during training, and an early stopping strategy was applied to prevent overfitting. In this way, the trained detector was able to accurately identify whether the face region in the input image had occlusion, achieving good classification results. \begin{table}
\begin{tabular}{l c} \hline \hline
**Occlusion Type** & **Proportion (\%)** \\ \hline Mask & 15\% \\ Sunglasses & 15\% \\ Mask + Sunglasses & 20\% \\ Mouth Color Block & 15\% \\ Eyes Color Block & 15\% \\ Mouth + Eyes Color Block & 20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Proportion of six occlusion types in the MORPH-2 dataset. \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**MAE**} \\ \cline{2-3}  & **Occlusion Dataset Direct Training** &
\begin{tabular}{c} **Layer-Wise Feature Reconstruction** \\ **+ Age Label Fine-Tuning** \\ \end{tabular} \\ \hline CORAL & 5.61 & 4.27 \\ DLDL & 5.91 & 4.43 \\ MWR & 5.98 & 4.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: MAE of the models before and after global fine-tuning with original age labels. To validate the cross-dataset performance of the model, we conducted random occlusion tests on the AFAD, CACD, and IMDB-WIKI datasets, comparing the results with several mainstream occluded face age estimation models. A group of human workers, who manually estimated ages from occluded facial images, served as the baseline for the age regression task. We compared the performance of human workers, DLP-CNN [48], Pix2 Pix [49], DeepFill v2 [50], LCA-GAN [1], MCGRL [6], and our methods (using CORAL, DLDL, and MWR as the underlying models) for occlusion-aware facial age estimation in terms of MAE and CS (%). All comparison models were retrained on the MORPH-2 dataset, with the occlusion types divided according to Table 7, ensuring consistency in the training conditions. For all methods, we performed tests on different datasets where the test set randomly applied various mixed occlusions to 50% of the facial images, while the other 50% remained unchanged. Table 9 shows that the KD-CORAL model, using CORAL as the age regressor, achieves the best performance in our experiments across multiple datasets. Additionally, we observed significant differences in MAE and CS performance among the various models across the three datasets. For all methods, the overall performance on different datasets follows the order AFAD > CACD > IMDB-WIKI. We attribute this to the fact that the CACD dataset includes some low-quality images (e.g., 20 \(\times\) 20 pixels) and has a wide age range (14-62 years), requiring the model to consider a broader age span. The IMDB-WIKI dataset, on the other hand, uses an automated labeling process, resulting in lower annotation quality and significant label noise.