Multi-branch CNN for Multi-scale Age Estimation

Convolutional Neural Networks (CNNs) attracted growing interest in recent years thanks to their high generalization capabilities that are highly recommended especially for applications working in the wild context. However CNNs rely on a huge number of parameters that must be set during training sessions based on very large datasets in order to avoid over-fitting issues. As a consequence the lack in training data is one of the greatest limits for the applicability of deep networks. Another problem is represented by the fixed scale of the filter in the first convolutional layer that limits the analysis performed through the subsequent layers of the network.