Improving Age Estimation in Occluded Facial Images with Knowledge Distillation and Layer-Wise Feature Reconstruction

With the widespread application of facial image-based age estimation technologies in fields such as marketing, medical aesthetics, and intelligent surveillance, their importance has become increasingly evident. However, in real-world scenarios, the facial images obtained are often incomplete due to occlusions caused by masks or sunglasses, which obscure the eyes, mouth, or nose to varying degrees. Such occlusions lead to the loss of critical facial feature information, thereby reducing the accuracy of age estimation. Although prior research has explored de-occlusion methods for occluded facial images, there remains a lack of studies focusing on the implicit facial feature information present in fixed occlusion patterns. To address this issue, this study proposes a novel method for reconstructing occluded facial features to enhance age estimation accuracy under occlusion conditions. This study introduces a facial feature reconstruction network based on knowledge distillation and feature reconstruction. The primary objective is to leverage complete facial information from a teacher model to guide a student network in fully extracting effective information from the unoccluded regions of occluded images. Additionally, the proposed method reconstructs feature maps of the occluded regions through a meticulous, layer-wise feature reconstruction process. The reconstructed network can then act as a feature encoder to provide more informative features for the age estimation regression module. Experimental results demonstrate that the proposed approach achieves superior performance in age estimation with randomly occluded images on the MORPH-2, AFAD, CACD, and IMDB-WIKI datasets, with mean absolute errors (MAE) of 4.27, 4.83, 5.15, and 5.71, respectively. These results outperform existing occluded facial age estimation methods based on attention mechanisms and generative facial image reconstruction.