## 4 Experimental Results
### Dataset and Experimental Setup  
In this section, we describe the dataset creation, preprocessing, and the configuration of the experimental setup. To evaluate the influence of various methods on facial age estimation under occlusion, a dedicated dataset featuring multiple types and degrees of occlusion was created. The dataset creation process is outlined as follows: four publicly available foundational datasets containing rich facial image data were selected--CACD [38], AFAD [20], MORPH-2 [39], and IMDB-WIKI [40]. The MORPH-2 [39] dataset consists of 55,608 facial images with age labels ranging from 16 to 70 years. It is a widely used dataset for age estimation tasks due to its extensive number of images and diversity in age, ethnicity, and gender. For our experiments, we split the dataset into two independent subsets: 80% for training and 20% for testing. The CACD dataset ([38]) contains 159,449 images, with ages ranging from 14 to 62 years. Similar to the MORPH-2 dataset, the faces in the CACD dataset are centered, with the tip of the nose aligned to the center. This dataset offers a broad spectrum of images, making it ideal for evaluating age estimation models. The AFAD dataset ([20]) contains 165,501 images of Asian faces, with ages ranging from 15 to 40 years. Since the faces in this dataset are already centered, no further alignment was necessary. The AFAD dataset is particularly useful for testing age estimation models on younger age groups, providing an additional important subset for evaluation. The IMDB-WIKI [40] dataset includes more than 500,000 facial images with age labels ranging from 0 to 100 years, covering a wide range of ethnicities, age groups, and facial expressions. It is divided into two parts: IMDB (460,723 images) and WIKI (62,328 images). For preprocessing, we used MTCNN (Multi-task Cascaded Convolutional Networks) [41] for facial landmark detection. The images were then cropped to center the faces and standardize their sizes. This preprocessing step ensures that the faces are aligned and their appearances remain consistent across different datasets. All models were trained on the MORPH-2 dataset, which contains a diverse range of facial images spanning multiple age groups. The remaining datasets (AFAD, CACD, IMDB-WIKI) were used to assess the model's performance across datasets. All experiments were conducted under the same hardware and software conditions to ensure comparability and fairness. Specifically, we used an NVIDIA 3080 GPU for computation, and PyTorch 1.12.1 was employed as the deep learning framework. In all experiments, the training process for all models followed identical hyperparameter settings, including the learning rate, optimizer, and number of training epochs. To comprehensively assess the impact of occlusion on face age estimation, we designed six different types of occlusion conditions (as illustrated in Figure 4): occlusion of the eye region using various types of sunglasses, blacking out the eye region, occlusion of the mouth region using different types of masks (including veils), blacking out the mouth region, simultaneous occlusion of both the eyes and mouth with glasses and masks, and simultaneous blacking out of both the eye and mouth regions with black blocks. Theseocclusion methods cover common facial obstructions in the real world. In real life, facial photos often lose parts of the image due to sunglasses and masks, while the application of black blocks can directly test the impact of occlusion on model performance without introducing noise variables.To ensure the accuracy of model performance analysis by minimizing the impact of the quantified occlusion area, we control the size of the mask and mouth blocks to cover 30-35% of the facial image area and the size of the sunglasses and eye blocks to cover 15-20% of the facial image area during the occlusion process. All images are uniformly resized to \(256\times 256\times 3\).