A Multi-Layer Multi-Channel Attentive Network For Gender And Age Recognition

In practical application, the existing gender and age recognition algorithms can't meet the requirements of both small-sized model and high accuracy simultaneously. Moreover, most models based on CNNs have a even larger size of more than 200M. In this paper a multi-layer multi-channel attentive network based on the idea of divide-and-conquer is proposed. This method uses multi-layer processes in each channel to extract features of different layers and improves accuracy by layer refinement. We use some dynamic parameters to fine-tune each layer to make the model fit better. Each layer uses the same classifier to reduce parameters to make the model smaller. And we import attention mechanisms to increase the ability of the network to use the features. Experiments show that the accuracy of this method is better than several mainstream networks and the size of the model is less than 0.5M, which can be used in mobile terminals well.