Age estimation is a classic learning problem in computer vision. Many larger and deeper CNNs have been proposed with promising performance, such as AlexNet, VggNet, GoogLeNet and ResNet. However, these models are not practical for the embedded/mobile devices. Recently, MobileNets and ShuffleNets have been proposed to reduce the number of parameters, yielding lightweight models. However, their representation has been weakened because of the adoption of depth-wise separable convolution. In this work, we investigate the limits of compact model for small-scale image and propose an extremely \textbf{C}ompact yet efficient \textbf{C}ascade \textbf{C}ontext-based \textbf{A}ge \textbf{E}stimation model(\textbf{C3AE}). This model possesses only 1/9 and 1/2000 parameters compared with MobileNets/ShuffleNets and VggNet, while achieves competitive performance. In particular, we re-define age estimation problem by two-points representation, which is implemented by a cascade model. Moreover, to fully utilize the facial context information, multi-branch CNN network is proposed to aggregate multi-scale context. Experiments are carried out on three age estimation datasets. The state-of-the-art performance on compact model has been achieved with a relatively large margin.

本研究提出了一种极其紧凑且高效的基于层叠上下文的年龄估计模型，它比MobileNets和ShuffleNets和VggNet只占1/9和1/2000的参数，同时具有竞争性能; 通过多分支CNN网络聚合多尺度上下文来充分利用面部上下文信息，实现了对年龄估计问题的重新定义。

C3AE：探索紧凑模型在年龄估计方面的极限