  Age estimation of face images is a crucial task with various practical
applications in areas such as video surveillance and Internet access control.
While deep learning-based age estimation frameworks, e.g., convolutional neural
network (CNN), multi-layer perceptrons (MLP), and transformers have shown
remarkable performance, they have limitations when modelling complex or
irregular objects in an image that contains a large amount of redundant
information. To address this issue, this paper utilizes the robustness property
of graph representation learning in dealing with image redundancy information
and proposes a novel Masked Contrastive Graph Representation Learning (MCGRL)
method for age estimation. Specifically, our approach first leverages CNN to
extract semantic features of the image, which are then partitioned into patches
that serve as nodes in the graph. Then, we use a masked graph convolutional
network (GCN) to derive image-based node representations that capture rich
structural information. Finally, we incorporate multiple losses to explore the
complementary relationship between structural information and semantic
features, which improves the feature representation capability of GCN.
Experimental results on real-world face image datasets demonstrate the
superiority of our proposed method over other state-of-the-art age estimation
approaches.
