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. 2 Related work People use the words 'baby face' or 'obsolete' to describe the gap between the real and apparent ages. Specifically, in the real world, real and apparent ages of people are inconsistent. It is not easy to judge the true age from a photograph. To address this problem, some scholars utilise neural networks to design lots of methods. These methods can be roughly divided into three kinds: classification, regression and ranking method. 2.1 Classification methods for age estimation In this kind of method, different ages are regarded as different categories. There are many examples of this kind of method.

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