Age Estimation Based on Graph Convolutional Networks and Multi-head
  Attention Mechanisms
Miaomiao Yang Changwei Yao Shijin Yan
Age estimation technology is a part of facial recognition and has been
applied to identity authentication. This technology achieves the development
and application of a juvenile anti-addiction system by authenticating users in
the game. Convolutional Neural Network (CNN) and Transformer algorithms are
widely used in this application scenario. However, these two models cannot
flexibly extract and model features of faces with irregular shapes, and they
are ineffective in capturing key information. Furthermore, the above methods
will contain a lot of background information while extracting features, which
will interfere with the model. In consequence, it is easy to extract redundant
information from images. In this paper, a new modeling idea is proposed to
solve this problem, which can flexibly model irregular objects. The Graph
Convolutional Network (GCN) is used to extract features from irregular face
images effectively, and multi-head attention mechanisms are added to avoid
redundant features and capture key region information in the image. This model
can effectively improve the accuracy of age estimation and reduce the MAE error
value to about 3.64, which is better than the effect of today's age estimation
model, to improve the accuracy of face recognition and identity authentication.