## 3 Proposed Methods  
### Overview of Suggested Method  
The method proposed in this paper is based on the following prior facts. In the problem of facial occlusion, occluding objects (such as sunglasses, masks, veils, etc.) typically cover specific regions of the face, primarily the eyes and mouth, and the positions and types of these occluding objects are usually fixed. As a result, there is a certain regularity between the occluded areas and the position of the occluding object. When humans see an image of a face with some occlusion, even if part of the face is occluded, we can still infer or imagine the complete facial image based on the visible (non-occluded) parts and estimate the apparent age of the person in the image. This indicates that, although the eyes and mouth may be occluded, the human brain can reasonably infer the features of the occluded parts by observing the non-occluded parts. Furthermore, under the premise that the occluding object covers fixed positions, there is an implicit mapping relationship between the features of the occluded and non-occluded parts. Even if some parts are occluded, as long as the features of the non-occluded parts are known, the features of the occluded parts can be inferred. For example, based on the forehead, chin, and facial contours, the features of occluded parts like the eyes and mouth can still be predicted. In traditional methods, the occluded parts are often ignored, and only the non-occluded parts of the face (such as the non-occluded facial region) are considered. Ignoring the important visual information of the occluded parts leads to the loss of crucial details in the image. Moreover,past methods have over-relied on age labels during the training process, while there is a lack of sufficiently annotated facial datasets with age labels, and obtaining such data is both expensive and time-consuming. More importantly, existing datasets often do not account for the various occlusion scenarios that are commonly found in the real world (such as masks, glasses, veils, etc. ), which causes models trained on these datasets to be unable to handle various facial occlusion problems in real-world scenarios. Based on these considerations, this study proposes a novel, integrated framework that optimizes the reconstruction of facial features under occlusion conditions specifically for age estimation tasks, using knowledge distillation and a layer-by-layer training strategy. The core of this framework lies in utilizing the rich feature representations in a pre-trained teacher model to guide the student model in learning the mapping relationship between occluded and non-occluded features across different dimensions, thereby reconstructing high-quality facial features. The layer-by-layer training strategy allows the model to gradually transition from learning simple low-level features (such as edges and textures) to more complex high-level features (such as facial contours and details), enhancing the model's ability to recover information in complex scenarios. To reduce over-reliance on age labels, this method employs a self-supervised training approach during the layer-by-layer knowledge distillation process, adding various occlusions to the facial images and using the original image itself as the supervisory signal for training the model. This means that, during training, the model does not need to rely on external age labels. Age labels are only used in the final global fine-tuning phase to eliminate the noise effects caused by occlusion. Three robust age estimation models--CORAL, DLDL, and MWR--are chosen as teacher models based on their complementary strengths: CORAL utilizes ordinal regression to capture age relationships, DLDL incorporates label distribution learning to account for age uncertainty, and MWR predicts both the mean and variance of age to provide confidence intervals. To ensure consistency between intermediate feature maps, the student model shares the same encoder architecture as the teacher models, eliminating the need for model compression.