## 2 Related Works
### Facial Occlusion in Age Estimation  
Despite considerable advances in age estimation, several challenges persist in real-world applications. Facial images in practical scenarios are often impacted by issues such as low resolution, poor lighting, noise, and occlusions. Key facial regions like the eyes, nose, and mouth are particularly susceptible to occlusions, which result in the loss of crucial features and hinder the accuracy of age estimation. Although various studies [29; 30; 31; 32] have explored occlusions caused by glasses, hats, scarves, and mobile devices, these approaches typically overlook the occluded areas, focusing instead on extracting features from the visible regions or attempting to generate the occluded regions from the unoccluded parts. However, these methods fail to fully utilize the relationships between occluded and visible facial features.  
Current de-occlusion techniques mainly address partial occlusions. For example, the Attention-based Partial Face Recognition method [33] combines intermediate feature maps from ResNet with attention pooling and an independent aggregation module, allowing the model to focus on the unoccluded facial regions. However, in cases of extensive occlusions (e.g., when both the eyes and mouth are covered), the attention mechanism is less effective, leading to a significant drop in performance. Another method proposed by [34] integrates attention mechanisms within a residual network to highlight key facial  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Categories** & **Method** & **Database** & **MAE** \\ \hline \multirow{4}{*}{Classification of multi-class ages} & \multirow{4}{*}{DEX [26]} & IMDB-WIKI + LAP2015 & 3.22 \\  & & MORPH & 2.68 \\  & & FG-NET & 3.09 \\  & & CACD & 6.52 \\ \hline \multirow{4}{*}{Regression based on metrics} & OR-CNN [20] & AFAD & 3.34 \\  & OR-CNN [20] & MORPH & 3.27 \\ \cline{2-4}  & \multirow{2}{*}{VGG + BridgeNet [27]} & MORPH & 2.38 \\ \cline{1-1}  & & FG-NET & 2.56 \\ \cline{1-1}  & & LAP2015 & 2.98 \\ \hline \multirow{4}{*}{Learning by the distribution of deep label} & \multirow{4}{*}{DLDL-v2 [28]} & LAP2015 & 3.14 \\ \cline{1-1}  & & LAP2016 & 3.45 \\ \cline{1-1}  & & MORPH & 1.97 \\ \hline Ranking & Ranking-CNN [21] & MORPH & 2.96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of age estimation methods.  
regions, such as the unoccluded eyes, and utilizes a mask generator to detect and clean occluded features. However, the mask generator struggles to generalize well under complex occlusion conditions, limiting its practical utility.  
Additionally, ref. [29] introduced a two-stage occlusion-aware GAN based on the Pix2 pix framework, which aims to remove occlusions by using randomly placed occlusion shapes. While the approach performs well in controlled scenarios, its applicability to more complex occlusions, such as those caused by the simultaneous occlusion of the eyes and mouth/nose, remains limited. Similarly, ref. [35] uses a two-stage GAN to remove medical mask occlusions and reconstruct the occluded facial regions. While it demonstrates notable improvements, issues such as color consistency and detail accuracy in the reconstructed regions persist. Furthermore, some studies [32] employ labeled occluded images for supervised learning, adding extra channels to detect and remove occluded regions. However, these methods typically rely on large annotated datasets and struggle to handle cases with regions where both the eyes and the mouth/nose are simultaneously occluded.