Fusion Network for Face-based Age Estimation
Abstract
Convolutional Neural Networks (CNN) have been applied to age-related research as the core framework. Although faces are composed of numerous facial attributes, most works with CNNs still consider a face as a typical object and do not pay enough attention to facial regions that carry age-specific feature for this particular task. In this paper, we propose a novel CNN architecture called Fusion Network (FusionNet) to tackle the age estimation problem. Apart from the whole face image, the FusionNet successively takes several age-specific facial patches as part of the input to emphasize the age-specific features. Through experiments, we show that the FusionNet significantly outperforms other state-of-the-art models on the MORPH II benchmark.
Cite
@article{arxiv.1807.10421,
title = {Fusion Network for Face-based Age Estimation},
author = {Haoyi Wang and Xingjie Wei and Victor Sanchez and Chang-Tsun Li},
journal= {arXiv preprint arXiv:1807.10421},
year = {2018}
}
Comments
ICIP 2018