On the use of DAG-CNN architecture for age estimation with multi-stage features fusion

Abstract

Accurate facial age estimation is quite challenging, since ageing process is dependent on gender, ethnicity, lifestyle and many other factors, therefore actual age and apparent age can be quite different. In this paper, we propose a new architecture of deep neural networks namely Directed Acyclic Graph Convolutional Neural Networks (DAG-CNNs) for age estimation which exploits multi-stage features from different layers of a CNN. Two instants of this system are constructed by adding multi-scale output connections to the underlying backbone from two well-known deep learning architectures, namely VGG-16 and GoogLeNet. DAG-CNNs not only fuse the feature extraction and classification stages of the age estimation into a single automated learning procedure, but also utilized multi-scale features and perform score-level fusion of multiple classifiers automatically. Fine-tuning such models helps to increase the performance and we show that even “off-the-shelf” multi-scale features perform quite well. Experiments on the publicly available Morph-II and FG-NET databases prove the effectiveness of our novel method.