## 4 Experimental Results
### Performance Verification Experiment
#### 4.2.3 Ablation Study  
As presented in Equation (1), we employed the mean absolute error (MAE), which is the most commonly used metric [43; 44], to assess the accuracy of age estimation. A lower MAE value indicates a higher accuracy in age estimation performance, where \(N\) is the total number of samples, \(y_{i}\) represents the actual age, and \(\hat{y}_{i}\) denotes the predicted age. A lower MAE value indicates better performance in age estimation. \[MAE=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-\hat{y}_{i}| \tag{1}\]  
The MAE values for the CORAL, DLDL, and MWR models, when directly tested on the unoccluded dataset, were 3.12, 3.23, and 3.39, respectively. However, upon testing on a dataset with 50% randomly occluded images, the MAE values increased to 5.62, 5.82, and 5.89, respectively. While all three models exhibited strong performance on the unoccluded dataset, the tests on the dataset with random occlusions revealed a substantial increase in the MAE for all models, indicating that facial occlusion significantly impairs the models' ability to estimate age accurately. Figure 5 visually illustrates the extent of the effect of random occlusion on model performance. In order to analyze the specific impact of different occlusion methods on model accuracy, we evaluated the performance of the three pre-trained baseline models under various occlusion conditions. The results demonstrated that the MAE values of the CORAL, DLDL, and MWR models, under different occlusion scenarios, reflect the varying effects of different occlusion types on model performance. For single occlusions, the MAE values when wearing a mask were 5.51 (CORAL), 5.72 (DLDL), and 6.44 (MWR), and, when wearing sunglasses, the MAE values were 6.21 (CORAL), 6.44 (DLDL), and 6.32 (MWR). When both a mask and sunglasses were simultaneously worn, the MAE values increased to 7.52 (CORAL), 7.71 (DLDL), and 7.82 (MWR). It was observed that the occlusion of the eyes had a more pronounced impact on model performance compared to the occlusion of the mouth and nose. When both types of occlusion were present, the performance of the models declined markedly. Figure 6 presents the impact of various occlusion methods on model performance. Furthermore, we examined the effect of using color blocks as a direct occlusion technique instead of physical occlusion objects. The experimental results indicated that the use of color block occlusion also led to a reduction in model accuracy, although the impact was slightly less severe compared to using physical occlusion objects. Our analysis suggests that the color block occlusion method introduces less complexity, whereas the use of diverse physical occlusion objects, with varying colors, textures, and shapes, introduces greater uncertainty, thereby introducing more complex noise to the model. Table 3 provides a comparison of the impact of all occlusion methods on model performance. Figure 5: The performance of the three baseline models on the original dataset and the occluded dataset. Figure 6: We tested the impact of various occlusion types, including masks, sunglasses, and masks + glasses, on the age estimation model. The figure shows the MAE performance of the CORAL, DLDL, and MWR models under different occlusion conditions. To validate the impact of layer-wise feature reconstruction on the performance of age estimation models, we tested the MAE values of three baseline models on the MORPH-2 dataset with 100% random occlusion. The experimental results show that, after layer-wise feature reconstruction, all three models achieved varying degrees of MAE reduction. This demonstrates that reconstructing occluded features provides age estimation models with more informative facial features, thereby effectively improving the accuracy of age estimation. Table 4 shows the effect of feature reconstruction on model accuracy.