Age Estimation Method Based on Generative Adversarial Networks

Abstract

Face age estimation has become a hot issue in the field of computer vision and man-machine interaction. To solve the unbalanced distribution of different types in age database, we adopted generative adversarial networks to study distribution of human face images to generate a great number of human face data of different ages. Then, we established age estimation model based on convolution neural network. Finally, in order to realize a better model performance, we adopted the training mode which was featured with knowledge transfer to improve performance of network models. The experimental results demonstrated that the proposed age estimation method has higher classification accuracy and a smaller age error.