## 1 Introduction  
Facial feature learning plays an essential role in a variety of computer vision tasks, with age estimation gaining increasing relevance in fields such as statistical marketing analysis tailored to product preferences among specific age groups, the beauty and telemedicine industries, and age-based suspect identification in intelligent surveillance systems [1, 2]. However, in practical applications, facial images often present significant challenges due to occlusions, which commonly disrupt the visibility of critical facial features [3]. Occlusion has been considered highly challenging. In real-life images or videos, facial occlusions can often be observed, e.g., facial accessories, including sunglasses, scarves, and masks [4]. These occlusions not only obscure important facial information but also undermine the accuracy of age estimation. Given that facial features evolve with age--such as changes in skin thickness and texture, the sharpening of bone lines, and the development of wrinkles--these variations differ substantially between individuals [5], making automatic age estimation (AAE) [6; 7; 8] under occlusion conditions a particularly difficult problem. Despite considerable advancements in age estimation algorithms, data collection, and system performance testing, as well as the establishment of rigorous evaluation protocols, improving the accuracy of age estimation continues to be a considerable challenge. Age estimation typically involves three key stages: feature representation, feature extraction, and age prediction. Early approaches primarily relied on handcrafted feature extraction, which required domain-specific knowledge. However, the effectiveness of such knowledge is often difficult to verify and can limit the generalizability of the model. In contrast, convolutional neural networks (CNNs) offer the advantage of automatically extracting distinct and robust facial features, while also learning age-related information directly from the data [9]. In CNN-based age estimation, convolutional layers are responsible for extracting and encoding age-related features from facial images, while multi-layer perceptrons (MLPs) use these features to predict the subject's age. A loss function measures the discrepancy between the predicted and true age, and backpropagation is employed to refine the model. This approach is fully automated and leverages features that are otherwise difficult to capture with prior knowledge. When compared to traditional techniques, CNN-based models have demonstrated superior performance in age estimation, and ongoing research continues to explore new methodologies to further improve their accuracy and effectiveness. To tackle the challenges posed by occlusions, existing research has explored a variety of solutions, with a primary focus on local feature learning [10], attention mechanisms [11], and generative models [1; 12]. Local feature learning methods partition the face into different regions and extract relevant features from the unoccluded areas, enabling age estimation even with partial occlusions. However, their performance degrades significantly when occlusion is more extensive. Attention mechanism-based techniques guide the model to concentrate on unoccluded regions while minimizing interference from occluded regions, though their effectiveness is strongly dependent on the size and shape of the occlusion. Generative models attempt to reconstruct complete facial information by inferring the unoccluded features. While these models can handle complex occlusions effectively, the generated regions often exhibit blurriness or distortion. Although these approaches have enhanced facial feature reconstruction under occlusion to some degree, they fail to fully exploit the relationships between occluded and unoccluded regions, which limits their accuracy in high-precision age estimation tasks.