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In this paper, we study lightweight age estimation methods based on a coarse-to-fine approach in which the network performs age prediction with multiple stages. In each stage, the network only focuses on refining the coarse age prediction generated from the previous stage. The final age prediction is the combination of all staged prediction values. We observe that these stages have a causal relationship, that is, the output of each stage is highly correlated with outputs of its former stages. Thus, each stage should share the information of its previous stage before making a refined prediction. Based on this observation, we construct a new compact CNN model called Homologous Stagewise Regression Network (HSR-Net). In HSR-Net, each stage shares the information of the last convolutional layer and then generates its own refined value. In addition, HSR-Net also addresses the age group ambiguity problem by utilizing an easy dynamic range construction. In order to enhance the prediction performance of HSR-Nets, it is naive to increase the number of kernels in each convolutional layer of HSR-Nets. However, the constructed HSR-Net has extremely large parameter size. To address this problem, we propose the separable HSR-Nets (SepHSR-Nets) where standard convolutions are replaced by depth-wise separable convolutions in the convolutional layers of HSR-Nets. In general, the parameter size of SepHSR-Nets ranges from 10K to 75K without sacrificing prediction performance. Experimental results show that SepHSR-Nets achieve competitive performance compared with the state-of-the-art compact models. Our code, data, and models are available at https://github.com/yanjenhuang/hsr-net.

New Coarse-to-Fine Approaches for Age Estimation based on Separable Convolutions