# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 1 Introduction  
Automatic age estimation is a crucial problem in biometrical recognition, and has plenty of practical applications, such as security verification, public-safety surveillance and border control. In [1], the authors illustrate that automatic age estimation supports the identification of undocumented individuals, accelerates the search for missing children and assists immigration officers in verifying the age of travelers or asylum applicants. Moreover, user profiling on social media, market analysis and human-computer interaction are further important application domains for automatic age estimation [2]. Although a lot of work has been made, effective human age estimation is still very challenging. As we all know, the aging process is slow and determined by many intrinsic and extrinsic factors, including identity, expression, pose illumination and so on. Therefore, there is an inherent ambiguity in the mapping between face image and its age label. Recent advances in age estimation have been made by deep learning models, which usually employ a one-stage network and can provide an overall estimation. However, it is still insufficient to perform accurate age estimation. As shown in Figure 1, the facial appearances tend to be very similar at adjacent ages, and errors frequently happen when distinguishing adjacent age labels. Despite the difficulties of accurate age estimation, it is relatively easy to achieve an accurate age range estimation of an unknown person and then perform finer age estimation within that range. Hierarchical age estimation is a successful method to perform the above from-coarse-to-fine decision strategy. A given face image is firstly classified into one of a number of age groups by a global estimation, then the precise age estimation is performed within the age group. Generally speaking, hierarchical age estimation is likely to result in better performance. Nevertheless, the existing works usually divide the age spectrum into several fixed age groups, such as [3,4], and improper quantization of age groups will also lead to large age estimation errors. On the other hand, the global estimation may provide prior knowledge to pick up some discriminative regions for better distinguishing adjacent age labels with subtle appearance differences. Therefore, refined age estimation from a small local range could benefit from more distinctive features learned from those age-sensitive areas. However, previous studies of hierarchical age estimation usually neglect the above guidance information. In brief, we aim to design a new two-stage model that overcomes the limitations of fixed age group partitioning and the underutilization of global information in local refinement found in previous methods. By introducing an adaptive age range in the first stage and leveraging a semantic attention mechanism in the second stage, our model dynamically adjusts to age-specific ranges and focuses on age-sensitive facial regions, enabling more precise and flexible age estimation. This paper proposes a novel two-stage deep model for age estimation. Our model sequentially performs a coarse estimation by relaxing the global age prediction to a local age range, and then refines the estimation from the local age range by semantic attention mechanism and fine-grained feature learning. At the first age estimation stage, an adaptive age range is obtained for each image by local adjustment of the global deep model prediction, which is learned based on Swin Transformer [5], to take advantage of global information during aging. At the second stage, we perform refined age estimation from local age range with the help of a semantic attention mechanism, which is a fine-grained classification task. The semantic attention aims to exploit global prediction as prior knowledge to locate more age-sensitive regions and features. During training, we define two losses for the two stages and jointly learn our model.