Three-stage network for age estimation

Abstract

Age estimation on the basis of the face has been widely used in the field of human–computer interaction and intelligent surveillance. Many existing methods extract deeper global features from the facial image and achieve significant improvement on age estimation. However, local features and their relationship are important for age estimation. In this study, the authors propose a model to use local features for age estimation. The proposed model consists of three stages, preliminary abstraction stage for extracting deeper features, local feature encoding stage to model the relationship between local features and recall stage for the combination of temporary local impressions. Extensive experiments show that their proposed method outperforms previous state-of-the-art methods.