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Age estimation technology is a part of facial recognition and has beenapplied to identity authentication. This technology achieves the developmentand application of a juvenile anti-addiction system by authenticating users inthe game. Convolutional Neural Network (CNN) and Transformer algorithms arewidely used in this application scenario. However, these two models cannotflexibly extract and model features of faces with irregular shapes, and theyare ineffective in capturing key information. Furthermore, the above methodswill contain a lot of background information while extracting features, whichwill interfere with the model. In consequence, it is easy to extract redundantinformation from images. In this paper, a new modeling idea is proposed tosolve this problem, which can flexibly model irregular objects. The GraphConvolutional Network (GCN) is used to extract features from irregular faceimages effectively, and multi-head attention mechanisms are added to avoidredundant features and capture key region information in the image. This modelcan effectively improve the accuracy of age estimation and reduce the MAE errorvalue to about 3.64, which is better than the effect of today's age estimationmodel, to improve the accuracy of face recognition and identity authentication.

Age Estimation Based on Graph Convolutional Networks and Multi-head Attention Mechanisms