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.

年龄估计技术是人脸识别的一部分，通过对游戏中的用户进行身份验证，实现了青少年防沉迷系统的开发和应用。本文提出了一种新的建模思想，使用图卷积网络（GCN）从非规则人脸图像中有效地提取特征，并添加了多头注意力机制以避免冗余特征并捕获图像中的关键区域信息，从而显著提高了年龄估计的准确性并降低了 MAE 误差值，进而提高了人脸识别和身份验证的准确性。