Age Classification Using Convolutional Neural Networks with the Multi-class Focal Loss

Abstract

Automatic age classification has drawn significant interest in plenty of applications such as access control, human-computer interaction, law enforcement and surveillance. Automatic age classification is a challenging task due to the complexity of facial images. A large number of approaches have been investigated on unconstrained datasets. However, most of these approaches have focused on the network architecture rather than the distribution of data, i.e., the extreme class imbalance existing among different age groups as the difficulty of data collection. In this paper, we propose a convolutional neural networks model based on the multi-class focal loss function. Specifically, our approach is designed to address the class imbalance via reshaping the standard cross entropy loss that it down-weights the loss assigned to well-classified examples. We validate our approach on well-known Adience benchmark. Finally, the experimental analysis shows that the proposed model achieves a significant improvement in performance for age classification.